Method for the Construction of Specific Promoters

ABSTRACT

The present application relates to a system for designing promoters for selective expression of genes. Thereby identified transcription regulatory elements are selected according to a specific methodology and used to create a library of transcription regulatory elements, which are then used to construct specific promoters, especially tissue-specific promoters.

FIELD OF THE INVENTION

The present application relates to a system for designing promoters for selective expression of genes.

DESCRIPTION OF RELATED ART

Cancer is a complex biological phenomenon that is thought to arise out of a multi-step process of genetic and epigenetic alterations in the cellular DNA, ultimately resulting in the transformation of the cell and its uncontrolled growth, division and migration. Identifying the aberrant molecular pathways that mediate cellular transformation has been a major challenge in understanding how malignancy develops.

The advent of functional genomics has given scientists the prospect of examining global changes in gene expression, providing molecular phenotypes that could potentially help in establishing more effective techniques of diagnosis and prognosis in a variety of cancers.

Utilising microarrays to decipher the molecular events that result in tumour progression has proven a mere difficult task, particularly since microarray data only provides a snapshot into a cell's transcriptome at a specific point in time. As many cancers contain multiple genetic alterations, it is difficult to ascribe specific changes in gene expression profiles to particular alterations in the genome of the transformed cell.

However, progress in the past few years has revealed that microarray data can have wider applications in the study of cancer, particularly with the advent of comparative genomic microarray analysis. In this type of analysis, gene expression data can be mapped to chromosomes, revealing potential sites of chromosomal aberrations, e.g. amplifications or deletions, which may predominate n particular types of cancer.

There is also now a growing trend for researchers to analyse microarray data in terms of ‘gene modules’ instead of the presentation of differentially regulated gene lists. By grouping genes into functionally related modules it is possible to identify subtle changes in gene expression that may be biologically (if not statistically significantly) important, to more easily interpret molecular pathways that mediate a particular response and to compare many different microarray experiments from different tumour types in an effort to uncover the commonalities and differences in multiple clinical conditions.

Therefore, we are moving into a new era of functional genomics, where the large datasets generated by the evaluation of global gene expression studies can be more fully interpreted by improvements in computational methods. It is important in the study of cancer that these improved bioinformatics tools be applied to this complex disease in an effort to unravel the molecular processes that mediate the malignant phenotype, so that ultimately improved targeted therapeutics can be effectively designed.

SUMMARY

Among the objects of the present invention is to provide a method and compositions for the construction of specific promoters. Such promoters can be specific for a type of cell, tissue, or condition, such as a particular disease or environmental condition, e.g., presence of a biological or chemical agent or microbial pathogen. Preferably these are promoters for a tissue-specific expression.

In certain embodiments, this aim is achieved by the inventions as claimed in the independent claims. Certain advantageous embodiments are described in the dependent claims, with other embodiments described herein.

Even if no multiple back-referenced claims are drawn, all reasonable combinations of the features in the claims shall be disclosed.

In certain aspects, the object of the invention is achieved by a method. In what follows, individual steps of a method will be described in more detail. The steps do not necessarily have to be performed in the order given in the text. Also, further steps not explicitly stated may be part of the method.

Provided are methods for selecting promoter elements and selecting and making promoter cassettes for cell-, tissue- or condition-specific expression, such as transcription-enhancing combined promoter cassettes. Such methods generally include a step of identifying or providing transcription factor regulatory elements (TFREs), such as a plurality of TFREs. Each of the plurality of TFREs is typically associated with one or more of a plurality of genes. Each of the plurality of genes generally is differentially expressed, for example, aberrently expressed, e.g., up-regulated or down-regulated, in a particular cell type or tissue type, or under a particular condition, for example, compared to another cell type, tissue type, or condition, such as a normal, control, or standard cell type or tissue or in the absence of the specified condition.

Among the cell and tissue types are eukaryotic cells, including animals, plants, fungi, and other eukaryotic cells. For example, the cell or tissue type can be from a mammal, yeast, insect, bovine, porcine, murine, equine, canine, feline, avian, piscine, ovine, insect, simian, and/or human.

In some aspects, the cell or tissue type is a tissue or cell from the cerebrum, cerebellum, adrenal gland, ovary, pancreas, parathyroid gland, hypophysis, testis, thyroid gland, breast, spleen, tonsil, thymus, lymph node, bone marrow, lung, cardiac muscle, esophagus, stomach, small intestine, colon, liver, salivary gland, kidney, prostate, blood, or other cell or tissue type.

In some aspects, the condition is a disease condition, such as a cancer, inflammatory disease, infectious disease, genetic defect, or other disease. The cancer can be cancer of the cerebrum, cerebellum, adrenal gland, ovary, pancreas, parathyroid gland, hypophysis, testis, thyroid gland, breast, spleen, tonsil, thymus, lymph node, bone marrow, lung, cardiac muscle, esophagus, stomach, small intestine, colon, liver, salivary gland, kidney, prostate, blood, or other cell or tissue type, and can include multiple cancers. For example, in some cases, each of the plurality of genes is aberrently regulated or differentially expressed in a number of different cancers.

In some aspects, the condition in the presence of a genetic defect, such as in the absence of a gene or portion thereof, in the presence of a particular genetic mutation, or in the absence of the function of a particular genetic pathway, such as in a genetically engineered cell or organism or in the presence of a naturally occuring mutation or genetic defect.

In other cases, the condition is an environmental condition. In some cases, the environmental condition is exposure to a particular drug, biological agent, chemical agent, or microbial pathogen. Such agents can include biologics, small molecules, antibodies and antibody fragments, fusion protein, recombinant proteins, nucleic acids, cytokines, ligands, and/or stimulatory materials derived or secreted from a particular cell culture or organism, such as from a tissue or fluid (e.g., blood or other bodily fluid) from a subject with a particular disease.

The determination that the plurality of genes is differentially expressed in the particular cell or tissue type or in the presence of the particular condition may be determined using well-known methods, such as the functional genomics applications described herein, including microarray or other analysis.

In one aspect, a TFRE is said to be associated with a given gene if it is within 20 kilobases, within 1C kilobases, within 5 kilobases, or within 4, 3, 2, or 1 kilobases of the gene; often, such TFREs are within the upstream region of the gene. In some aspects, each of the selected TFREs is within the upstream region of more than fifty percent of the plurality of genes. In some embodiments, a TFRE is considered associated with a given gene only if it is present in the sense strand or only if it is present in the antisense strand. In one embodiment, the TFRE is considered associated with a given gene only if it is present in the sense strand. If not otherwise specified, a TFRE is considered associated if present in the sense or antisense strand.

The methods typically further include selecting one or more TFRE from among the plurality of TFREs. In one aspect, each of the selected TFREs is within close proximity, such as within 20 kilobases, within 10 kilobases, within 5 kilobases, or within 4, 3, 2, or 1 kilobases, of more than fifty percent of the plurality of genes. In some aspects, each is within 10 kilobases or 5 kilobases, preferably within the upstream region of, more than 50 percent of the plurality of genes. In some aspects, each of the selected TFREs is within the upstream region of more than fifty percent of the plurality of genes.

The selection of TFREs is typically further based upon the frequency and length of the TFREs, and the relationship thereof.

Frequency of a TFRE relates to the frequency if its occurrence in association with the plurality of genes, as defined herein. In some cases, the frequency may be set forth in terms of the frequency within a given proximity to any of the plurality of genes, i.e., the number of occurrences of a given TFRE within such proximity to any of the plurality of genes, divided by the total number of TFRES within that proximity to any of the plurality of genes. For example, frequency of a given TFRE within 20 kilobases of the plurality of genes would be defined as the number of occurrences of that TFRE within 20 kilobases of any of the genes divided by the number of occurrences of all TFREs within 20 kilobases of any of the plurality of genes.

In some embodiments, the frequency is set forth in terms of association of the TFRE with the gene in the sense or antisense strand; in other embodiments, it is set forth in terms of the frequency of association of the TFRE with the gene in the sense strand. For example, in some embodiments, frequency is the frequency of a given TFRE within a given proximity, e.g., 20 kilobases, of a plurality of genes, in the sense strand; in other embodiments, frequency is the frequency of a given TFRE within a given proximity, e.g., 20 kilobases, of a plurality of genes, in the sense or antisense strand. Unless otherwise specified, frequency refers to the occurrence in either the sense or antisense strand.

Length refers to the length in nucleotides of a given TFRE.

Frequency and length generally are used to select TFREs according to the following equation:

frequency^((1/length)).

The value of this equation is called the SYN value. TFREs typically are selected based on their SYN values. In some embodiments, each of the selected TFREs has a SYN value (as defined by the above equation) of at least or larger than 0.1, 0.2, typically of at least or larger than 0.3, 0.4, 0.5, 0.6, 0.7, or 0.8, such as or at or about 0.3, 0.4, 0.5, 0.6, 0.7, or 0.8, typically at least at or about or above 0.5 or 0.6. In other embodiments, SYN values are used to rank the TFREs, with the TFREs having the highest SYN values selected. In some aspects, each of the selected TFRES is within the TFREs having the top 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or, 50 SYN values of the plurality of TFRES, typically having a SYN value within the top 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10.

In some embodiments, the methods include making or designing promoter-cassettes, such as a transcription-enhancing combined promoter cassette. In such embodiments, the methods can further include constructing a library of randomly combined selected TFREs or randomly combined elements. In one aspect, the library is made by mixing individual double stranded DNA sequence elements encoding at least the selected TFREs together under ligation reaction conditions. Such a library may be made by randomly ligating together double stranded DNA oligonucleotides, each of which may contain a DNA sequence element encoding at least the selected transcription factor regulatory elements, preferably the selected transcription factor regulatory elements, together under ligation conditions.

In some examples, the methods further include inserting combined TFREs or elements of the Library into a vector or into multiple vectors, such as a vector with a minimum promoter and typically a reporter gene, thereby producing a combined promoter cassette. In some embodiments, multiple vectors are used, thereby producing a plurality of combined promoter cassettes. In one aspect, the reporter gene is LacZ or GFP. In some examples, the methods further include inserting the vector into a host cell.

In some aspects, elements present in the library and/or combined promoter cassette contain at least or more than 70, 75, 80, 85, 86, 67, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identity to one of the selected TFREs, preferably 100% identity to one of the selected TFREs. In some aspects, the combined promoter cassettes contain at least 70, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identity to one of SEQ ID NOs: 130 to 191 or its complement. In some aspects, they contain at least 70, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identity to one of SEQ ID NOs: 5 to 66 or its complement.

In particular examples, the combined promoter cassettes or promoters contain at least 70, 75, 80, 85, 66, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 identity to SEQ ID NO: 50, SEQ ID NO: 113, SEQ ID NO: 175, or SEQ ID NO: 237. In particular examples, the combined promoter cassettes or promoters contain at least 70, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 identity to SEQ ID NO: 24, SEQ ID NO: 87, SEQ ID NO: 149, or SEQ ID NO: 211. In particular examples, the combined promoter cassettes or promoters contain at least 70, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identity to SEQ ID NO: 26, SEQ ID NO: 89, SEQ ID NO: 151, or SEQ ID NO: 213. In particular examples, the combined promoter cassettes or promoters contain at least 70, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 identity to SEQ ID NO: 59, SEQ ID NO: 122, SEQ ID NO: 184, or SEQ ID NO: 246. In particular examples, the combined promoter cassettes or promoters contain at least 70, 75, 80, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% identity to SEQ ID NO: 65, SEQ ID NO: 128, SEQ ID NO: 190, or SEQ ID NO: 252.

The randomly combined sequence elements may be cut with a restriction enzyme and cloned upstream of a reporter gene, which can be without limitation GFP or LacZ, and a library of plasmid DNA or viral vector may be generated. The library can be produced using without limitation retroviral vectors or adenoviral vectors.

In some examples, a plurality of host cells is produced. In such cases, the methods can further include screening for combined promoter cassettes made by the method, e.g., by screening for a host cell as produced in the method that shows enhanced expression of the reporter gene. Typically, such promoter cassettes are selected.

In certain aspects, the methods further include identifying the combined promoter cassette in the host cell produced by the method, or the host cell or cells selected by the screening step as described above. In some aspects, the identification includes determining the sequence or part of the sequence of the combined promoter cassette or the TFRE(s) therein.

In some cases, synthetic promoters and cassettes having a greater number of TFREs per promoter is advantageous, such as by generating a more effective promoter. Thus, in one embodiment, where a plurality of combined promoter cassettes are generated by the method, the method further includes selecting one or more of the combined promoter cassettes so produced. In one aspect, the number of TFREs per promoter in each of the selected combined promoter cassettes is greater than the average number of TFREs per promoter for the plurality of combined promoter cassettes originally generated by the method. In another example, the number of TFREs per promoter is greater than 1, 2, 3, 4, 5, 6, 7, 8, or more per promoter, or is 1, 2, 3, 4, 5, 6, 7, 1, or more per promoter preferably greater than 2, 3, 4, 5, 6, 7, or 8 or more per promoter or 2, 3, 4, 5, 6, 7, 8, or more per promoter. The TFRE can be present in the sense or the antisense strand of the promoter cassettes.

In one embodiment, the vector DNA containing the library of random sequence combinations cloned upstream of a minimum promoter followed by the reporter gene may be transfected or infected into the target cells and sorted with FACS (fluorescence activated cell sorting) for the selection of cells expressing high levels of the reporter gene. Sorted cells are then used to recover and amplify the vector DNA containing the desired high performing transcription regulatory element combination.

The vectors recovered and amplified from the sorted cells may be used for another round of screening for transcription regulatory element combinations with even higher performance and/or activity in a different type of host cell.

Upon the completion of repeated sorting and selection, recovered DNA vectors may further be screened individually in the target cells to test for their true promoter activity.

As a control, if cell-type specific promoters are desired, the selected vectors containing the transcription regulatory element combinations may also be tested in non-target cells for the purpose of eliminating vectors with substantial promoter activities in non-target cells.

Also provided are the combined promoter cassettes as produced by such methods, and vectors, libraries, and cells containing the same and methods for using the same. In some cases, the promoter cassettes include randomly combined TFREs, a minimum promoter, and a reporter gene. In some embodiments, each of the TFREs in the combined promoter cassette is within 20, 10, or 5 kilobases, preferably within the upstream region, of more than fifty percent of a plurality of genes identified as being differentially expressed in a particular cell type or tissue type or under a particular condition and has a SYN value larger than 0.3, 0.2, 0.1, 0.4, or 0.5, where the SYN value is defined as described above. The vector can be a plasmid, viral, transiently expressed, or integrated into the genome of a host cell.

Also provided are isolated promoters for driving and/or regulating expression. To some embodiments, such promoters contain an isolated nucleic acid as given in one of the SEQ ID Nos. 130 to 191 or the complement of one of SEQ ID Nos. 130 to 191. Thus, provided are promoters containing the nucleotide sequence set forth in any of SEQ ID NOs: 130-191 or the antisense sequence (i.e., complement) thereof. In other embodiments, they contain an isolated nucleic acid having at least or more than 70, 75, 80. 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% sequence identity, such as at least 90% sequence identity with, the sequence of any of SEQ ID Nos. 130 to 191. In other embodiments, they contain isolated nucleic acid capable of specifically hybridising under stringent conditions with a DNA sequence as given in one of SEQ ID Nos. 130 to 191. In other embodiments, such promoters can include such an isolated nucleic acid that is further interrupted by an intervening sequence or a fragment of such a nucleic acid sequence capable of driving and/or regulating expression.

The isolated promoters may comprise further a minimal promoter, like Muc-1 minimal promoter. In some embodiments, such promoters contain an isolated nucleic acid as given in one of the SEQ ID Nos. 5 to 66 or the complement of one of SEQ ID Nos. 5 to 66. Thus, provided are promoters containing the nucleotide sequence set forth in any of SEQ ID NOs: 5-6 or the antisense sequence (i.e., complement) thereof. In other embodiments, they contain an isolated nucleic acid having at least or more than 70, 75, 80. 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% sequence identity, such as at least 90% sequence identity with, the sequence of any of SEQ ID Nos. 130 to 191. In other embodiments, they contain isolated nucleic acid capable of specifically hybridising under stringent conditions with a DNA sequence as given in one of SEQ ID Nos. 5 to 66. In other embodiments, such promoters can include such an isolated nucleic acid that is further interrupted by an intervening sequence or a fragment of such a nucleic acid sequence capable of driving and/or regulating expression.

Also provided are genetic constructs including such isolated promoters, a heterologous nucleic acid sequence operatively linked to such a promoter. Such constructs can optionally include a 3′ transcription terminator.

Also provided are host cells including any one or more of the vectors, isolated promoters, and/or genetic constructs described above.

Also provided are methods for driving and/or regulating expression using the provided promoters, constructs, vectors, and cells. In one aspect, such methods include driving or regulating expression of a nucleic acid in a cell. Such methods can be carried out by operably linking such nucleic acid to any of the isolated promoters described above or made using the described methods, and introducing the resultant genetic construct into a cell.

Also provided are uses of the above-described promoters to drive and/or regulate expression of an operably linked nucleic acid.

DETAILED DESCRIPTION OF THE INVENTION Definitions

As used herein, “transcription regulatory element,” “TRE,” transcription factor regulatory element,” and “TFRE” refer to a nucleotide sequence that is recognized by a transcription regulator, and is synonymous with “cis-acting sequence” or “cis-acting sequence element” or “cis-acting region”, and sometimes expressed as “sequence element”.

As used herein, “combined transcription regulatory element” refers to a double stranded DNA molecule that includes more than one transcription regulatory element. The combined transcription regulatory element may be created by ligating various double stranded transcription regulatory elements in a random fashion. Optionally, the combined sequence element may contain a spacer region and the length of the spacer nucleotides may be controlled by subjecting the double stranded DNA molecules to time-course exonuclease digestion before using them in random ligation reactions.

As used herein, “oligonucleotide” refers to a sequence that functionally includes a cis-acting region and perhaps up to about 25 or less extraneous nucleotides. Therefore, the number of nucleotides that are encompassed by the term “oligonucleotide” cannot be fixed, and therefore is not limited to any particular number of nucleotides.

As used herein, “promoter cassette” or “synthetic promoter cassette” refers to DNA segment that contains components for an efficient transcription of a gene, and may include one or more transcription regulatory element, a minimum promoter region, sequences from 5′-untranslated region or introns.

As used herein, “minimum promoter region” or “minimum promoter” refers to a short DNA segment which is inactive by itself, but can mediate strong transcription when combined with other transcription regulatory elements. Minimum promoter sequence can be derived from various different sources, including prokaryotic and eukaryotic genes. Examples of this are dopamine beta-hydroxylase gene minimum promoter and cytomegalovirus (CMV) immediate early gene minimum promoter.

As used herein, “combined promoter cassette” or “synthetic combined promoter cassette” refers to promoter cassettes containing combined transcription regulatory elements.

As used herein, a “transcription regulator” refers to any factor including proteins that bind to the cis-acting region and regulate either positively or negatively the expression of the gene. Transcription factors or repressors or co-activators or co-repressors are all included.

The advances in functional genomics made in recent years have resulted in the identification of many more cis-regulatory elements that can be directly related to the increased transcription of specific genes. Indeed, the ability to use bioinformatics to unravel complex transcriptional pathways active in diseased cells can actually serve to facilitate the process of choosing suitable cis-elements that can be used to design synthetic promoters in complex pathologies such as cancer.

In cancer the changes in the gene expression profile are often the result of alterations in the cell's transcription machinery induced by aberrant activation of signalling pathways that control growth, proliferation and migration. Such changes result in the activation of transcription regulatory networks that are not found in normal cells and provide us with an opportunity to design synthetic promoters that should only be active in cancerous cells.

If microarray technology is to truly result in the design of tailored therapies to individual cancers or even patients, as has been heralded, it is important that the functional genomics methodology that was designed for the identification of signalling and transcription networks be applied to the design of cancer-specific promoters so that effective gene therapeutic strategies can be formulated.

The development of bioinformatics algorithms for the analysis of microarray datasets has largely been applied in order to unravel the transcription networks operative under different disease and environmental conditions. To this date there has been no effort to use this type of approach to design synthetic promoters that are operative only under these certain disease or environmental conditions.

Described herein are methods whereby data obtained from functional genomics experiments, such as microarray analysis, are analysed using widely available bioinformatics software tools, which function to find over-represented cis promoter elements, in order to design synthetic promoters that are only active in cancer cells. This represents a major leap forward in the design of cancer-specific promoters that can subsequently be used in the study of cancer, or in the design of safe and effective genetic therapy of human malignancies.

Rational Promoter Selection and Design

In one aspect, the provided methods and compositions are based on the application of functional genomics in the development of synthetic promoters, for example, for the control of gene expression in specific environmental conditions, for example, in a disease- or tissue-specific manner, or in response to the introduction of an external agent, either chemical or biological. Synthetic promoters have been constructed for use in a number of systems, both prokaryotic and eukaryotic.

Available methods for designing synthetic promoters for eukaryotic systems involve the arbitrary selection of well-characterised cis-regulatory elements, spanning 50 to 100 nucleotides. Such elements then are included in synthetic promoter libraries created by random ligation and selected for in the cell type of interest (Li, X., Eastman, E. M., Schwartz, R. J., & Draghia-Akli, R. Synthetic muscle promoters: activities exceeding naturally occurring regulatory sequences. Nat. Biotechnol. 17, 241-245 (1999); Dai, C., McAninch, R. E., & Sutton, R. E. Identification of synthetic endothelial cell-specific promoters by use of a high-throughput screen. J. Virol. 78, 6209-6221 (2004)).

In one aspect, the provided methods and compositions apply functional genomics and advanced bioinformatics approaches to the sequence of the human genome to design synthetic promoters in a rational manner. Generally, in the provided methods, transcription factor regulatory elements (TFREs), such as cis-regulatory elements, are selected for inclusion in synthetic promoter libraries in a non-arbitrary fashion. In some aspects, the methods use information from global gene expression analyses to identify TFREs e.g., cis-regulatory elements) associated with specific gene expression profiles, allowing for the weighting and ranking of regulatory elements and the development of improved methods of selection.

Thus, methods provided herein are advantageous compared to methods that arbitrarily select cis-elements for use in synthetic promoters. In some aspects, the provided methods are capable of selecting shorter cis-regulatory elements compared to those selected by available methods. In some aspects, the provided methods identify cis-elements that were previously not known to be involved in the transcriptional network for which cis-elements are being selected, or would not be selected using a random selection approach. In some aspects, the provided methods generate synthetic promoters that are shorter, more complex, and/or contain more cis-regulatory elements, compared to available methods.

The regulation of gene expression in eukaryotes is highly complex and often occurs through the coordinated action of multiple transcription factors. The use of trans-factor combinations in the control of gene expression allows a cell to employ a relatively small number of transcription factors in the regulation of disparate biological processes.

As discussed herein, a number of tools are available for use with the provided methods to utilise microarray data to identify TFREs, such as cis-regulatory elements. It is also possible to use this information to decipher the transcriptional networks that are active in cells under different environmental conditions. In yeast, the importance of the combinatorial nature of transcriptional regulation was established by specifically examining clusters of upregulated genes for the presence of combinations of cis-elements. By examining microarray data from yeast exposed to a variety of conditions, it is possible to construct a network of transcription revealing the functional associations between different regulatory elements. This approach resulted in the identification of key motifs with many interactions, suggesting that some factors serve as facilitator proteins assisting their gene-specific partners in their function.

Thus, a core number of transcription factors mediate such a vast array of biological responses by adopting multiple configurations. In one aspect, the provided methods use this observation to hijack the transcriptional programs that have gone awry in multifactorial diseases, such as cancer, to develop disease-specific or condition-specific regulatory elements. In cancer, for example, methods of interpreting cancer microarray data are continually evolving so that a more global picture of transcriptional regulation in transformed cells can now be painted.

Meta-analyses of cancer datasets has permitted the identification of gene modules, allowing for the reduction of complex cancer signatures to small numbers of activated transcription programs and even to the identification of common programs that are active in most types of cancer. This type of analysis can also help to identify specific transcription factors whose deregulation plays a key role in tumour development. For instance, in one study, the importance of aberrant E2F activity in cancer was reaffirmed during a search for the regulatory programs linking transcription factors to the target genes found upregulated in specific cancer types (Rhodes, D. R. et al. Mining for regulatory programs in the cancer transcriptome. Nat. Genet. 37, 579-583 (2005)). It was shown that E2F target genes were disproportionately upregulated in more than half of the gene expression profiles examined, which were obtained from a multitude of different cancer types. It was thus proposed that integrative bioinformatics analyses have the potential to generate new hypotheses about cancer progression.

In some aspects, the present invention is based on the discovery that the elucidation of disease-specific transcriptional programs allows construction of synthetic conditional promoter elements that can be used in gene therapy to drive restricted gene expression in pathologic sites of interest. Provided are methods that use integrative computational approaches to identify transcriptional programs active in specific diseases, in certain eukaryotic cell types, and/or under particular environmental conditions, for example, cancer indications.

Such methods are useful for the design synthetic promoter elements to drive gene expression in the particular diseases, cell types, tissue types, and/or one or more environmental conditions, for example, for use in therapeutic approaches.

In one example, provided are methods for rational design of synthetic promoter elements that drive highly cyotoxic genes, and anti-cancer therapeutic approaches employing the same. In one aspect, microarray data obtained by experimentation, or taken from publicly available resources such as Oncomine, may be used in order to identify the regulatory sequences overrepresented in clusters of genes found to be upregulated in cancer stem cells.

Bioinformatics Tools

Different bioinformatics tools, examples of which are given in table 1, may be used to screen for TFREs, e.g., cis-regulatory elements. In general, such tools function by comparing gene expression profiles between differentially regulated genes and examining upstream sequences, available through genome sequence resources. For the phylogenetic footprinting tools, the untranslated regions of specific genes are compared between species and the most highly conserved sequences are returned and proposed to be potential cis-elements. A combination of all available approaches may be employed in order to identify regulatory sequences that predominate in the profile of specific cell or tissue types, for example in cancer stem cells. The most common sequences identified are then used as the building blocks employed in the design of synthetic promoters.

Typically the data used for the identification of genes aberrantly regulated in cancer cells is derived from microarray data. These methods can provide detailed information on the regulation of specific genes. It may further be necessary to screen the genes identified for false positives, e.g. the overexpression may be a result of altered transcription factor activation instead of chromosomal amplification.

Cells or Tissues of Interest

The invention relates in certain aspects to assays carried out on a cell or tissue type of interest. In particular, the provided methods and compositions relate to TFREs associated with gene expression in the context of one or more diseases, conditions, environmental conditions, cell type, e.g., eukaryotic cell type, tissue types, and/or following exposure to a particular agent, such as a biological agent, e.g., ligand, chemical agent, or microbial pathogen.

The cell types can include any type of cell, or plurality of cells such as a tissue. Cells and tissues for use with the provided methods include prokaryotic cells and tissues, typically eukaryotic cell, cells and tissues. A suitable eukaryotic cell may be derived from an organism, such as an animal, such as a mammal and preferably a human, or another eukaryotic organism, such as a plant. Such a cell or tissue may have been taken directly from such an organism or may be derived therefrom. For example, the cell or tissue may be from a primary, secondary or immortalised cell line or culture that is derived from such an organism.

The cell or tissue may be a naturally occurring cell or tissue or may have been artificially manipulated. For example, a cell or tissue may be manipulated by exposure to altered environmental or disease-specific conditions. For example, a cell or tissue may be manipulated by exposing it to an agent, such as a biological ligand, chemical agent or microbial pathogen.

The biological ligand nay be any biological molecule that is capable of having an effect on the cell, particularly an effect on genie transcription. A biological ligand may be a molecule that is capable of binding to the cell or acting within the cell. A biological ligand may, for example, be a polypeptide, protein, nucleic acid or carbohydrate molecule. Suitable biological ligands include hormones, growth factors and neurotransmitters.

The chemical agent may be any agent capable of acting on the cell, preferably leading to a change in gene transcription within the cell. The chemical agent may, for example, be a chemotherapeutic drug or a therapeutic small molecular drug.

The microbial pathogen may be any virus, bacteria, fungus or other infectious agent capable of causing disease in mammals.

The cell or tissue may from an abnormal or disease source. For example, the cell or tissue may be taken from, or derive from, an organism suffering from a disease. Preferably the cell or tissue is from a tissue or organ that is affected by the disease. For example, where the disease is cancer, the cell or tissue may be taken from a tumour. The cell may be from, or derived from, a tumour cell line in vitro.

Among the cell and tissue types are eukaryotic cells, including animals, plants, fungi, and other eukaryotic cells. For example, the cell or tissue type can be from a mammal, yeast, insect, bovine, porcine, murine, equine, canine, feline, avian, piscine, ovine, insect, simian, and/or human.

In some aspects, the cell or tissue type is a tissue or cell from the cerebrum, cerebellum, adrenal gland, ovary, pancreas, parathyroid gland, hypophysis, testis, thyroid gland, breast, spleen, tonsil, thymus, lymph node, bone marrow, lung, cardiac muscle, esophagus, stomach, small intestine, colon, liver, salivary gland, kidney, prostate, blood, or other cell or tissue type.

In some aspects, the condition is a disease condition, such as a cancer, inflammatory disease, infectious disease, genetic defect, or other disease. The cancer can be cancer of the cerebrum, cerebellum, adrenal gland, ovary, pancreas, parathyroid gland, hypophysis, testis, thyroid gland, breast, spleen, tonsil, thymus, lymph node, bone marrow, lung, cardiac muscle, esophagus, stomach, small intestine, colon, liver, salivary gland, kidney, prostate, blood, or other cell or tissue type, and can include multiple cancers. For example, in some cases, each of the plurality of genes is aberrently regulated or differentially expressed in a number of different cancers.

In some aspects, the condition is in the presence of a genetic defect, such as in the absence of a gene or portion thereof, in the presence of a particular genetic mutation, or in the absence of the function of a particular genetic pathway, such as in a genetically engineered cell or organism or in the presence of a naturally occuring mutation or genetic defect.

In other cases, the condition is an environmental condition. In some cases, the environmental condition is exposure to a particular drug, biological agent, chemical agent, or microbial pathogen. Such agents can include biologics, small molecules, antibodies and antibody fragments, fusion protein, recombinant proteins, nucleic acids, cytokines, ligands, and/or stimulatory materials derived or secreted from a particular cell culture or organism, such as from a tissue or fluid (e.g., blood or other bodily fluid) from a subject with a particular disease.

Transcription Factor Regulatory Elements

Among the provided methods are those that involve the identification of transcription factor regulatory elements (TFREs) that are active in a cell, cells, tissue, and/or disease or condition (e.g., environmental condition or disease condition, exposure to a particular agent, such as a biological agent, e.g., ligand, chemical agent, or microbial pathogen) of interest. Also provided are the TFREs, and promoters and libraries containing the same.

A suitable transcription factor regulatory element (TFRE) for use or selection in the provided methods, compositions, promoters, and libraries is a nucleic acid molecule that is recognised by a transcription factor. For example, a TFRE may comprise a sequence to which a transcription factor can bind. A TFRE may comprise a cis-acting region. By transcription factor is meant any factor, such as a protein, that can bind to such a cis-acting region and regulate either positively or negatively the expression of a gene. For example, a transcription factor may bind upstream of the coding sequence of a gene to either enhance or repress transcription of the gene by assisting or blocking RNA polymerase binding. Many transcription factors are well known in the art and include STAT, E2F, Oct-4, Nanog, Brachury, Pax genes, Sox2 and MCEF.

A TFRE comprises a nucleic acid sequence preferably, a double stranded DNA sequence. The TFRE may comprise a cis-acting region and may also comprise additional nucleic acids. The core six to eight nucleotides of promoter and enhancer elements may be sufficient for the binding of their corresponding transactivating factors. Indeed, in some cases this short oligonucleotide element is sufficient to drive gene expression alone.

Thus, a transcription factor binding site may consist of 6 to 8 nucleic acids. A TFRE comprising that site will be at least 6 to 8 nucleic acids in length. In some embodiments, a TFRE of the invention is preferably 6 or more, 8 or more, 10 or more, 15 or more, 20 or more, 25 or more, or 30 or more nucleic acids in length. In some aspects, the provided embodiments provide synthetic promoters that are shorter compared to available synthetic promoters. In some embodiments, the TFRE is 100 or less, 75 or less, 50 or less, less than 50, 30 or less, 25 or less, 20 or less or 15 or less nucleic acids in length, preferably any combination of the given upper and lower values, preferably 6 to 100 or 6 to 25 nucleic acids.

Identification of TFREs

A suitable TFRE is one that is active in the cell or tissue of interest or under the condition of interest. Such a TFRE may be identified as being associated with a gene that is expressed in the cell or tissue of interest.

For example, a TFRE may be associated with a gene that is differentially expressed in that cell, tissue, or condition, when compared with another cell, tissue or condition. For example, differential expression of a gene may be seen by comparing the expression of the gene in two different cells, tissues, or conditions, and/or in the same cells or tissues under different conditions. Expression in one cell or tissue type may be compared with that in a different, but related, tissue type. For example, where the cell or tissue of interest is a disease cell or tissue or has been artificially manipulated as described herein, the expression of genes in that cell or tissue may be compared with the expression of the same genes in an equivalent normal or untreated cell or tissue. This may allow the identification of genes that are differentially regulated between the two cell or tissue types or under different conditions.

A TFRE that is associated with such a gene is generally located close to the coding sequence of the gene within the genome of the cell. For example, such a TFRE may be located in the region immediately upstream or downstream of that coding sequence. Such a TFRE may be located close to a promoter or other regulatory sequence that regulates expression of the gene. The location of a TFRE may be determined by the skilled person using his knowledge of this field and the methods described herein.

Suitable TFREs may thus be identified by analysis of the cell or tissue of interest and/or under the particular condition of interest. Genes that are differentially expressed in the cell or tissue of interest may be identified by routine methods. For example, routine methods may be used to compare the expression profile of genes in the cell or tissue of interest with that in other cell or tissue types which may act as a control. Genes that are up-regulated or down-regulated in the cell or tissue of interest may thus be identified. Such an analysis may make use of, for example, micrcarray analysis or serial analysis of gene expression (SAGE).

Such an analysis may be carried out using a sample of expressed molecules from the cell or tissue of interest or using all the expressed molecules from the cell or tissue of interest. For example, in one embodiment, such an analysis may be carried out using the total RNA content of the cell or tissue of interest. Thus, in some embodiments, the methods of the invention may be used to analyse expression from the entire genome of the cell or tissue of interest.

Such an analysis may be used to assess the expression of a wide variety of genes, or a subgroup of genes. Thus, in accordance with embodiments of the present invention, a selection of genes may be used that is known to be regulated by a wide variety of different transcription factors or each gene by only one or two transcription factors.

The ability to use gene expression data to identify gene modules, which mediate specific responses to environmental stimuli (or to a diseased state) and to correlate their regulation to the cis-regulatory elements present upstream of the genes in each module, has transformed the way in which microarray data are interpreted. For instance, by using the modular approach it is possible to examine whether particular gene modules are active in a variety of different cancers, or whether individual cancers require the function of unique gene modules. This allows screening for transcriptional commonalities between different cancers, which should aid in the design of widely applicable anti-cancer therapeutic strategies. In Gene expression data from 1975 microarrays, spanning 22 different cancers, was used to identify gene modules that were activated or deactivated in specific types of cancer (Segal, E., Friedman, N., Koller, D., & Regev, A. A module map showing conditional activity of expression modules in cancer. Nat. Genet. 36, 1090-1098 (2004)). A bone osteoblastic module was active in a number of cancers whose primary metastatic site was known to be the bone. Id. Thus, a common mechanism of bone metastasis between varieties of different cancers was identified, which could be targeted in the development of anticancer therapies.

It is also possible to identify the higher-level regulator that controls the expression of the genes in each module (Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166-176 (2003)). Examination of the upstream regulatory sequences of each gene in a module may reveal the presence of common cis-regulatory elements that are known to be the target of the module's regulator. Therefore, by identifying specific regulatory proteins that control the activation of gene modules in different cancers, it should be possible to extrapolate the important cis-elements that mediate transcription in the transformed cell. Thereby, allowing, for example, the design and construction of tumour-specific promoters based on the most active cis-regulatory elements in a number of tumour-specific gene modules.

Thus, once the differential expression of genes in a cell or tissue of interest or under certain conditions has been established, the sequences proximate to the differentially-expressed genes, such as those upstream of the differentially expressed genes, may be screened for TFREs, such as cis-regulatory elements. Those cis-regulatory elements which control expression of differentially expressed genes are considered to be active in the cell or tissue of interest. Thus, for those cis-elements to be active, the transcription factor(s) which control their activity must be present in that cell type. This therefore allows the identification of TFREs that are active in the cell or tissue of interest.

TFREs, cis-elements, may be identified using known methods, for example by screening using known bioinformatics techniques.

The ability to identify specific transcriptional elements in the human genome that control the expression of functionally related genes is transforming the application of functional genomics. Until recently the interpretation of data from microarray analysis has been limited to the identification of genes whose function may be important in a single pathway or response. How this related to global changes in the cellular phenotype had been largely ignored, as the necessary tools to examine this simply did not exist. With the advancement of bioinformatics we are now in a position to utilize all the data that is obtained from large-scale gene expression analysis and combine it with knowledge of the completed sequence of the human genome and with transcription factor, gene ontology and molecular function databases, thereby more fully utilizing the large datasets that are generated by global gene expression studies.

For nearly two decades scientists have been compiling databases that catalogue the trans-factors and cis-elements that are responsible for gene regulation (Wingender, E. Compilation of transcription regulating proteins. Nucleic Acids Res 16, 1879-1902 (1988)). This has resulted in the emergence of useful tools, such as TRANSCompel (Kel-Margoulis, O. V., Kel, A. E., Reuter, I., Deineko, I. V., & Wingender, E. TRANSCompel: a database on composite regulatory elements in eukaryotic genes. Nucleic Acids Res 30, 332-334 (2002)), ABS (Blanco, E., Farre, D., Alba, M. M., Messeguer, X., & Guigo, R. ABS: a database of Annotated regulatory Binding Sites from orthologous promoters. Nucleic Acids Res 34, D63-D67 (2006)), JASPAR (Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W. W., & Lenhard, B. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res 32, D91-D94 (2004)), HTPSELEX (Jagannathan, V., Roulet, E., Delorenzi, M., & Bucher, P. HTPSELEX—a database of high-throughput SELEX libraries for transcription factor binding sites. Nucleic Acids Res 34, D90-094 (2006)) and TRANSFAC (Matys, V. et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res 31, 374-378 (2003)) that index transcription factors and their target sequences based on experimental data, and TRED (Zhao, F., Xuan, Z., Liu, L., & Zhang, M. Q. TRED: a Transcriptional Regulatory Element Database and a platform for in silico gene regulation studies. Nucleic Acids Res 33, D103-D107 (2005)), which indexes based on both experimental and automated data.

Databases of known transcription factor binding sites can be used to detect the presence of protein-recognition elements in a given promoter, but only when the binding site of the relevant DNA-binding protein and its tolerance to mismatches in vivo is already known. Because this knowledge is currently limited to a small subset of transcription factors, it can be advantageous to discover regulatory motifs by comparative analysis of the DNA sequences of Promoters. By finding conserved regions between multiple promoters, motifs can be identified with no prior knowledge of transcription factor binding sites.

A number of models have emerged that achieve this by statistical overrepresentation. These algorithms function by aligning multiple untranslated regions from the entire genome and identifying sequences that are statistically significantly over-represented in comparison to what it expected by random, e.g. YMF (Sinha, S. & Tompa, M. YMF: A program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 31, 3586-3588 (2003); Sinha, S. & Tompa, M. Discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 30, 5549-5560 (2002)) and SCORE (Rebeiz, M., Reeves, N. L., & Posakony, J. W. SCORE: a computational approach to the identification of cis-regulatory modules and target genes in whole-genome sequence data. Site clustering over random expectation. Proc. Natl. Acad. Sci. U.S.A 99, 9888-9893 (2002)). At present these tools are mainly applied the study of lower eukaryotes where the genome is less complex and regulatory elements are easier to identify, extending these algorithms to the human genome has proven somewhat more difficult.

In order to redress this issue a number of groups have shown that it is possible to mine the genome of higher eukaryotes by searching for conserved regulatory elements adjacent to transcription start site motifs such as TATA and CAAT boxes, e.g. as catalogued in the DBTSS resource (Suzuki, Y., Yamashita, R., Sugano, S., & Nakai, K. DBTSS, DataBase of Transcriptional Start Sites: progress report 2004. Nucleic Acids Res 32, D78-D81 (2004); Suzuki, Y., Yamashita, R., Nakai, K., & Sugano, S. DBTSS: DataBase of human Transcriptional Start Sites and full-length cDNAs. Nucleic Acids Res 30, 326-331 (2002)), or one can search for putative cis-elements in CpG rich regions that are present in higher proportions in promoter sequences (Davuluri, R. V., Grosse, I., & Zhang, M. Q. Computational identification of promoters and first exons in the human genome. Nat. Genet. 29, 412-417 (2001)).

Alternatively, with the co-emergence of microarray technology and the complete sequence of the human genome, it is now possible to search for potential transcription factor binding sites by comparing the upstream non-coding regions of multiple genes that show similar expression profiles under certain conditions. Gene sets for comparative analysis can be chosen based on clustering, e.g. hierarchical and k-means (Roth, F. P., Hughes, J. D., Estep, P. W., & Church, G. M. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol. 16, 939-945 (1998)), from simple expression ratio (Bussemaker, H. J., Li, H., & Siggia, F. D. Regulatory element detection using correlation with expression. Nat. Genet. 27, 167-171 (2001)) or functional analysis of gene products (Jensen, L. J. & Knudsen, S. Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation. Bioinformatics. 16, 326-333 (2000)). This provides scientists with the opportunity to identify promoter elements that are responsive to certain environmental conditions, or those that play a key role in mediating the differentiation of certain tissues or those that may be particularly active in mediating pathologic phenotypes.

Phylogenetic footprinting, or comparative genomics, is now being applied to identify novel promoter elements by comparing the evolutionary conserved untranslated elements proximal to known genes from a variety of organisms. The availability of genome sequences between species has notably advanced comparative genomics and the understanding of evolutionary biology in general. The neutral theory of molecular evolution provides a framework for the identification of DNA sequences in genomes of different species.

Its central hypothesis is that the vast majority of mutations in the genome are neutral with respect to the fitness of an organism. Whilst deleterious mutations are rapidly removed by selection, neutral mutations persist and follow a stochastic process of genetic drift through a population. Therefore, non-neutral DNA sequences (functional DNA sequences) must be conserved during evolution, whereas neutral mutations accumulate. Initial studies sufficiently demonstrated that the human genome could be adequately compared to the genomes of other organisms allowing for the efficient identification of homologous regions in functional DNA sequences.

Subsequently, a number of bioinformatics tools have emerged that operate by comparing non-coding regulatory sequences between the genomes of various organisms to enable the identification of conserved transcription factor binding sites that are significantly enriched in promoters of candidate genes or from clusters identified by microarray analysis.

Examples of these software suites include TRAFAC (Jegga, A. G. et al. Detection and visualization of compositionally similar cis-regulatory element clusters in orthologcus and coordinately controlled genes. Genome Res 12, 1408-1417 (2002)), CORG (Dieterich, C., Wang, H., Rateitschak, K., Luz, H., & Vingron, M. CORG: a database for Comparative Regulatory Genomics. Nucleic Acids Res 31, 55-57 (2003)), CONSITE (Lenhard, B. et al. Identification of conserved regulatory elements by comparative genome analysis. J. Biol. 2, 13 (2003)), CONFAC (Karanam, S. & Moreno, C. S. CONFAC: automated application of comparative genomic promoter analysis to DNA microarray datasets. Nucleic Acids Res 32, W475-W484 (2004)), VAMP (La Rosa, P. et al. VAMP: visualization and analysis of array-CGH, transcriptome and other molecular profiles. Bioinformatics. 22, 2066-207 (2006)) and CisMols Analyser (Jegga, A. G. et al. CisMols Analyzer: identification of compositionally similar cis-element clusters in ortholog conserved regions of coordinately expressed genes. Nucleic Acids Res 33, W408-W411 (2005)). Typically these tools work by aligning the upstream sequences of target genes between species thus identifying conserved regions that could potentially function as cis-regulatory elements and have consequently been applied in the elucidation of transcription regulatory networks in a variety of models.

A significant amount of effort has been dedicated to the cataloguing of transcription factors and their corresponding cis-elements. More recently, these databases have been compiled with the aim to utilise them to unravel regulatory networks active in response to diverse stimuli. Some examples of these resources include PreMod (Blanchette, M. et al. Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression. Genome Res 16, 656-668 (2006); Ferretti, V. et al. PReMod: a database of genuine-wide mammalian cis-regulatory module predictions. Nucleic Acids Res 35, D122-D126 (2007)), CisView (Sharov, A. A., Dudekula, D. B., & Ku, M. S. CisView: a browser and database of cis-regulatory modules predicted in the mouse genome. DNA Res 13, 123-134 (2006)), BEARR (Vega, V. B., Bangarusamy, D. K., Miller, L. D., Liu, E. T., & Lin, C. Y. BEARR: Batch Extraction and Analysis of cis-Regulatory Regions. Nucleic Acids Res 32, W257-W260 (2004)), VISTA (Dubchak, I. & Ryaboy, D. V. VISTA family of computational tools for comparative analysis of DNA sequences and whole genomes. Methods Mol. Biol. 338, 69-89 (2006)), FromAn (Lardenois, A. et al. PrcmAn: an integrated knowledge-based web server dedicated to promoter analysis. Nucleic Acids Res 34, W578-W583 (2006)), CRSD (Liu, C. C. et al. CRSD: a comprehensive web server for composite regulatory signature discovery. Nucleic Acids Res 34, W571-W577 (2006)) and MPromDb (Sun, H. et al. MPromDb: an integrated resource for annotation and visualization of mammalian gene promoters and ChIP-chip experimental data. Nucleic Acids Res 34, D98-103 (2006)).

Table 1 lists some of the currently available databases that can be used when searching for potential regulatory sequences. This table provides an example of the type of resource utilised when identifying potential cis-acting sequences.

Thus, any of the databases listed in Table 1, or any equivalent publicly available resource, may be used to identify TFREs, such as cis-regulatory elements, that are associated with genes that are expressed in the cell or tissue of interest, preferably genes that are differentially expressed in the cell or tissue of interest. Preferably at least one database selected from Pubmed, DBTSS, TRAFAC, TRANSCompel, TRANSFAC, Phylofoot, CORG, CONSITE, CONFAC, CisMols, TRED, ABS, JASPAR, HTPSELEX, PAINT, PreMOD, CisView, BEARR, VISTA, PromAn, CRSD, MPromDb, VAMP and Oncomine is used.

In the provided methods, the transcription regulatory elements are identified within the sequences in proximity of the plurality of genes, for example, genes whose expression is associated with the cell type, tissue type, or condition of interest. Typically the sequences are within a region of 20 kbases up- or downstream of each gene selected, preferably 10 kbases, more preferably 3 kbases, most preferred upstream within each of these regions.

In some embodiments of the provided methods, to facilitate the rational selection of transcription regulatory elements and their ranking in order of importance, the frequency of occurrence of each sequence is calculated.

As used herein, frequency is defined as the number of occurrences of a given transcription factor regulatory element (TFRE) being associated with any of the plurality of genes (i.e., the number of times a TFRE is associated with any of the plurality of genes), divided by the total number of transcription factor regulatory elements associated with any of the plurality of genes. To some embodiments, the TFRE is considered associated with a given gene for purposes of calculating frequency whether it is present in the sense or antisense strand, i.e., whether it is in the forward or reverse direction. In another embodiment the TFRE is considered associated with a given gene only if it is present in the sense strand or only if it is present in the antisense strand. In one embodiment, the TFRE is considered associated with a given gene for purposes of calculating frequency only if it is present in the sense strand. Unless otherwise specified, the TFRE is considered associated if present in either strand. In some embodiments, the frequency is defined in terms of the desired proximity to the plurality of genes. In such cases, the frequency is the number of occurrences of a given TFRE within the desired proximity, e.g., 20, 10, or 5 kilobases of any of the plurality of genes, divided by the total number of all the TFREs within such proximity to the plurality of genes. For example, it a transcription regulatory element occurs 150 times within the desired regions (e.g., within 20, 10, or 5 kb up or downstream of any of the plurality of genes) and a total number of 5000 sites of transcription regulatory elements were identified within that proximity for the plurality of genes, the frequency of this transcription regulatory element would be 150/5000. Again, in some embodiments, the occurrence is on the sense or antisense strand, i.e. in the forward or reverse direction; in other embodiments, the occurrence is only on the sense strand or only on the antisense strand.

Given that conservation of longer sequences is a good indication of their importance in mediating expression, weight is added to longer cis-regulatory elements using the relation frequency^((1/lenghth)), with frequency being defined as described above and length being the length in nucleotides of the transcription regulatory element The calculated value (which is the nth root of the frequency, wherein n is the length) is also called SYN-value herein.

The SYN-value generally is used as a selection criterion, together with a threshold value, to allow the rational selection of TFREs, e.g., input cis-regulatory elements. The threshold SYN value can be any number between 0.1 and 0.9. Preferably the SYN-value has to be larger than the threshold; thus, in certain embodiments, TFREs are selected which have SYN values greater than or at least 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9 or about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9. It is also possible to select a predefined number of TFREs, e.g., cis-acting sequences, e.g. 1 to 10 genes, which have the largest SYN-values. Thus, in some embodiments, among a plurality of TFREs identified as proximate to the plurality of genes, those selected are those having the top 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 highest SYN values, as defined by the formula above.

In a preferred embodiment a threshold value is larger than 0.3, preferably larger of 0.4, more preferred larger of 0.5. In another preferred embodiment the threshold value has the value of 0.5.

Construction of Synthetic Promoters

In recent years some efforts nave been made to construct synthetic promoters for tissue specific transcription based on the linking of short oligonucleotide promoter and enhancer elements in a random fashion.

In one approach, which aimed to identify synthetic promoters for muscle-specific expression, duplex oligonucleotides from the binding sites of muscle-specific and non-specific transcription factors were randomly ligated and cloned upstream of a minimal muscle promoter driving luciferase (Li, X., Eastman, E. M., Schwartz, R. J., & Draghia-Akli, R. Synthetic muscle promoters: activities exceeding naturally occurring regulatory sequences. Nat. Biotechnol. 17, 241-245 (1999)). Approximately 1000 plasmid clones were individually tested by transient transfection into muscle cells and luciferase activity was determined in 96-well format by luminometry. By this approach several highly active and muscle specific promoters were identified that displayed comparable strength to the most commonly used viral promoters such as CMV.

In an effort to examine one million clones, Sutton and coworkers adopted a different screening approach based on the establishment of a lentiviral vector-based library (Jai, C., McAninch, R. E., & Sutton, R. E. Identification of synthetic endothelial cell-specific promoters by use of a high-throughput screen. J. Virol. 78, 6209-6221 (2004)). In this study duplex oligonucleotides from binding sites of endothelial cell-specific and non-specific transcription factors were cloned in a random manner upstream of a minimal promoter driving expression of eGFP in a HIV self-inactivating expression vector. A pool of one million clones was then transfected into endothelial cells and the highest expressers were selected by FACS sorting. Synthetic promoters were then rescued from stable transfectants by PCR from the genomic DNA where the HIV vectors had integrated.

The results from this study also demonstrated the possibility of isolating several highly active endothelial cell-specific synthetic promoter elements from a random screen.

When adopting this type of methodology in the design of synthetic tissue-specific promoters it is important to use well-designed duplex oligonucleotides. For example, each element has to be spaced in such a way that the regulatory elements appear on the same side of the DNA helix when reassembled, relevant minimal promoter elements have to be employed so that the screen produces promoters capable of expressing efficiently only in the tissue of interest and there must be some sort of mechanism, such as the addition of Sp1 sites, for the protection against promoter silencing through methylation.

The random nature of this approach actually increases the chance of finding active tissue-specific promoters, given that in some studies, where synthetic promoters were designed rationally by the linking of whole promoter regions rather than individual promoter elements, actually result in the identification of less efficient tissue-specific promoters. Therefore, the ability to carefully select relevant promoter/enhancer elements that will yield efficient tissue-specific promoters by these methods is paramount to the success of this approach.

Thus, in embodiments of the present invention, two or more selected TFREs as described above may be combined together as part of a synthetic promoter. A promoter element includes a DNA sequence that includes components that allow for the transcription of a gene.

A promoter element may include one or more transcription regulatory elements, a minimum promoter region and sequences from the 5′ untranslated region of the gene or introns. In one embodiment, a promoter element may also comprise one or more cis-elements that allow the binding of one or more ubiquitously expressed transcription factors. A promoter element may comprise one or more regulatory elements that allow for transient gene expression. A promoter element may comprise one or more regulatory elements that allow for inducible gene expression.

As used herein, a minimal promoter refers to a DNA sequence which is inactive alone, but can mediate gene transcription when combined with other transcription regulatory elements. Minimal promoter sequences can be derived from various sources, such as prokaryotic and eukaryotic genes. Examples of minimal promoters include the dopamine beta-hydroxylase promoter and the cytomegalovirus (CMV) immediate early gene minimal promoter.

According to one aspect of the present invention, two or more TFREs are combined with a minimal promoter in a single promoter element. This may be achieved by mixing a number of TFREs as described herein under ligation reaction conditions. The TFREs may be directly linked to each other. The TFREs may be separated by spacer nucleotides. For example, the TFREs may be separated by 1 or more, 2 or more, 5 or more, 10 or more or 20 or more nucleotides. These spacer nucleotides may be for example 1 to 20 nucleotides.

In an embodiment of the invention the oligonucleotides encoding the TRFEs further comprise a short overhang of single stranded nucleotides, which is able to hybridize with the overhang of another oligonucleotide. Such an overhang may be also between 1 to 20 nucleotides long.

The TFREs combined in this way nay be identified by a method described herein or may already have been identified as being active in the cell or tissue of interest.

A promoter element preferably contains two or more TFREs. The number of TFREs in each promoter element may be variable, or each promoter element may comprise the same number of TFREs. A promoter element may comprise 2 or more, 3 or more, 4 or more, 5 or more, or 6 or more TFREs. Preferably the ligated oligonucleotides from 0.1 to 1 kB are selected for the library, e.g. from an agarose gel. The sequence of a TFRE in the promoter element may be present in the sense or the antisense strand (i.e., in the forward or reverse direction) of the promoter element. The same TFRE may be present multiple times.

The promoter element may be arranged so that the TFREs are located upstream to the minimal promoter. Alternatively, the TFREs may be located downstream to the minimal promoter.

Expression Vectors

A plurality of promoter elements as described herein is used to create a library of expression vectors. Each expression vector comprises an antibiotic resistance gene. For example, expression of the gene may confer resistance to neomycin, zeocin, hygromycin or puromycin. A promoter element as described herein is included in a vector such that it is operably linked to the gene. That is, the promoter element is located such that it is capable of expressing the coding sequence of the gene in a cell of interest. The vector preferably includes no promoter or regulatory sequences other than those present in the promoter element. This ensures that any gene transcription from the promoter must have been regulated by the promoter element introduced into the vector.

The vector may be any vector capable of expression of an antibiotic resistance gene in the cell or tissue of interest. For example, the vector may be a plasmid or a viral vector. The vector may be a vector that integrates into the host genome, or a vector that allows gene expression while not integrated.

A plurality of different vectors as described herein may be provided. These may form a library. For example, where analysis of differential expression as described above has led to the identification of multiple TFREs for a cell or tissue type of interest, a plurality of promoter elements may be produced which comprise those TFREs. A mixture of multiple copies of the TFREs may be combined to produce a variety of different promoter elements. These may each be included in a vector to produce a library of vectors for the cell or tissue type of interest.

Assay Methods

A library of vectors as described herein may be assayed for vectors that are capable of expressing the antibiotic resistance gene in the cell or tissue of interest. Briefly, such an assay will comprise the steps of: transfecting cells of the cell or tissue of interest with vectors from the library; culturing said cells under conditions suitable for gene expression; and screening the cells for antibiotic resistance.

Transfection may be achieved using any suitable method. A variety of transfection methods are known in the art and the skilled person will be able to select a suitable method depending on the type of vector and type of cell or tissue that it is desired to use.

The culturing step may involve maintaining the transfected cells under suitable conditions to allow gene expression to occur. Where an inducible regulatory sequence has been included in the promoter elements, it may also be necessary to expose the cells or tissues to the relevant inducing agent.

The relevant antibiotic should then be added to the medium. In those cells where the promoter element does contain a suitable combination of TFREs to allow gene expression, the antibiotic resistance gene will be expressed and the cells will be resistant to the application of the antibiotic. For example, where the cell or tissue of interest includes the particular combination of transcription factors needed to activate the cis-acting factors within the promoter element, that promoter element may be capable of regulating expression of the antibiotic resistance gene.

In those cells where the promoter element does not contain a suitable combination of TFREs to allow gene expression, the cell will not have antibiotic resistance and will be killed by the presence of antibiotic. For example, where the cell or tissue of interest does not include the correct transcription factors, or does not include those transcription factors at sufficient levels to allow the cis-acting elements to regulate gene expression, the antibiotic resistance gene may not be expressed.

This will allow the selection of those cells in which the promoter element is capable of regulating gene expression in the cell or tissue type of interest. It is possible that mutations are introduced to the cis-acting elements during this step.

In one embodiment, the method may comprise a further step. In order to determine whether the activity of such a promoter element is specific to the cell or tissue type of interest, a further assay step may be carried out to determine whether the antibiotic resistance gene will also be expressed when the vector is transfected into a different cell type. For example, where the cell or tissue of interest has been treated with a particular biological ligand, chemical agent or microbial pathogen, the activity of the promoter element may also be assessed in untreated cells to determine whether the promoter element will be generally active in that cell typo or only on those cells following such a treatment. Similarly, where the cell or tissue type is a diseased tissue, such as a cancer cell type, the activity of the promoter element in a “normal” equivalent tissue type may be assessed to determine whether the promoter element is generally active in that tissue type, or only in the disease state.

Two examples of strategies that may be adopted in the design and construction of synthetic promoter elements are as follows:

Bacterial Library Approach.

Regulatory elements corresponding to the transcription programs found to be upregulated in cancer cells using comparative genomics and integrative bioinformatics approaches detailed above are randomly ligated together with a minimal promoter upstream of the antibiotic selection gene in a promoter-less mammalian expression vector. Duplex oligonucleotides are designed so that when linked together the regulatory elements are present on the same face of the double helix and contain Sp1-elements to prevent promoter silencing by methylation. The oligonucleotides that represent promoter elements are ligated together using different ratios and each ligation mix typically comprises five or six different cis-elements. Resultant plasmid constructs are then used to transfect corresponding cancer cell lines in 96-well format in order to find the optimal promoters by antibiotic selection, and promising candidate promoters are isolated and sequenced before being further transfected into control cell lines in order to ascertain tumour cell specificity. Clones containing synthetic promoters that display restricted expression in cancer cell lines are then selected.

Retroviral Library Approach.

Duplex oligonucleotides are designed as described above and are ligated into a self-inactivating (SIN) mouse moloney retroviral vector containing a minimal promoter driving the expression of the antibiotic selection gene. Bacterial clones are pooled and a mixed library of retroviral vectors is constructed and used to stably transduce selected cancer cell lines. Cancer cells are infected so that only 50% of the cells express the antibiotic selection gene and very high concentrations of antibiotic are used to sort the strongest expressing cells from the remaining population. Single clones of cancer cell lines transduced with the optimal synthetic promoter elements are then isolated by dilution cloning approaches. Genomic DNA is isolated, the synthetic promoter rescued by PCR and cloned into a promoter-less mammalian expression vector containing eGFP to evaluate expression in control cell lines thus confirming tumour specificity.

Uses for Identified Promoter Elements

The invention also extends to promoter elements and vectors of the invention, such as promoter elements and vectors that have been identified by the methods of the invention and to their uses.

Promoter elements or vectors identified by the methods of the invention as being active in a cell or tissue type of interest may be used to target genes to that cell or tissue type. For example, where the methods of the invention show that a promoter element is active specifically in a particular cell type, but not in a control cell type, then that promoter element may be used to specifically direct expression in the cell type or tissue type, or order the condition, of interest.

Thus, a promoter element of the invention may be combined with a gene that it is desired to express in a particular cell type. For example, a vector may be produced in which a promoter element of the invention is operably linked to the coding sequence of a gene. That vector may then be used to transfect a cell of interest. The vector may be any vector type as described herein, for example a plasmid or a viral vector. Alternatively, such a vector may be produced by replacing the antibiotic resistance gene in a vector identified by a method of the invention with the gene of interest.

Thus, provided is a method of expressing a gene in a cell or tissue of interest or under a particular condition, comprising the steps of: identifying a promoter element capable of regulating gene expression in the cell or tissue or under the particular condition, for example, using the provided methods for identifying promoter elements; generating an expression vector comprising said promoter element operably linked to a gene; and transfecting the cell or tissue with the vector and allowing gene expression to occur.

These methods may be used in vitro to manipulate cells in culture. For example, gene expression in an in vitro cell population may be manipulated using a promoter element of the invention.

These methods may be used in vivo to manipulate cells in a human or animal body or other eukaryotic organism, such as a plant. For example, a promoter element or vector of the invention, such as a promoter element or vector that has been identified as described herein as being capable of regulating gene expression in a cell or tissue of interest, may be provided for use in a method of therapy or diagnosis to be carried out on the human or animal body or organism. Such a promoter element or vector may be used in the manufacture of a medicament for the therapeutic treatment of the cell or tissue of interest. For example, where the cell or tissue of interest is from a disease tissue such as cancer, then the promoter element or vector may be used for the treatment of that disease, such as cancer. For example, the promoter element or vector may be used to direct expression in the particular disease tissue of a polypeptide having a therapeutic effect. Thus, in some embodiments, the invention may be used to provide a method of treating a disease such as cancer, the method comprising delivering a promoter element or vector of the invention, such as a promoter element or a vector that has been identified by a method of the invention, to a patient suffering from said disease, wherein the promoter element or vector directs expression in the disease cells or tissue of a therapeutic agent.

Another object of the invention is to provide a method of selecting multiple promoter elements whose combination is capable of specifically regulating gene expression under a particular condition or in a particular cell or tissue, for example, in a cancer cell, by performing the selection steps for the transcriptional regulatory elements described in the previous described method.

In one aspect of such methods, in the first step, a plurality of transcription factor regulatory elements (TRFEs) that are associated with any of a plurality of genes whose expression is identified as being aberrantly regulated in a particular cell or tissue type or under a particular condition, for example, in cancer cells, such as in cancer cells from a number of different sources, are provided or are identified.

From this plurality of transcription factor regulatory elements, particular TFREs are selected according to a number of pre-defined criteria. One criterion generally is that the selected transcription factor regulatory elements have to be in close proximity to more than fifty percent of the plurality of genes for example, within 20, 10, or 5 kb of more than 50 ii of the genes and typically within the upstream region of such genes). The term proximity is thereby defined as the transcription factor regulatory element being located within the region of 20 kbases up- or downstream of the gene associated with, preferably 10 kbases, even more preferably 5 kbases, most preferably within the upstream region. The TFRE may be considered associated with a given gene whether its sequence is present on the sense or antisense strand (i.e., in the forward or reverse direction). In one embodiment only TFREs on the sense strand (i.e., in the forward direction) are considered to be associated with a given gene.

Another criterion generally relates to the frequency of occurrence of the TFREs. The frequency is defined as described herein above. Selection generally also relates to the length in nucleotides of each transcription factor regulatory element. Frequency and length generally are used to identify candidate regulatory elements (selected TFREs) according to the following relationship:

frequency^((1/length)).

As discussed above, the calculated value (which is the nth root of the frequency, wherein n is the length) is also called SYN-value, which generally is used as a selection criterion and/or to rank the TFREs. The threshold SYN value can be any number between 0.1 and 0.9. Preferably the SYN-value of selected TFREs has to be larger than the threshold; thus, in certain embodiments, selected TFREs have SYN values greater than or at least 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9 or about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, or 0.9. It is also possible to select a predefined number of TFREs, e.g., cis-acting sequences, e.g. 1 to 10 genes, which have the largest SYN-values. Thus, in some embodiments, among a plurality of TFREs identified as proximate to the plurality of genes, those selected are those having the top 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, or 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 highest SYN values, typically within the top 10 SYN values.

Another object of the invention is vector comprising the combined promoter cassette obtained the prescribed methods, wherein the vector is a plasmid, viral, transiently expressed or integrated into the genome of a host cell.

Such a combined promoter cassette contains 2 or more, 3 or more, 4 or more, 5 or more, or 6 or more TFREs, originally selected for the method, wherein the sequence of each TFRE within the promoter cassette have a homology of more than 70%, preferably more than 80% to the sequence of the TFRE originally selected. The TFRE can be present in the sense or in the antisense strand of the promoter cassette. In a preferred embodiment the promoter cassette also contains a minimum promoter. In a preferred embodiment the TFREs in the sequence are selected from the TFREs shown in table 3 and/or their complements.

Another object of the invention is a host cell comprising the prescribed a vector. The preferred embodiment the host cell is a prokaryotic or a eukaryotic cell, preferably a mammalian cell.

Another object of the invention is an isolated promoter capable of driving and/or regulating expression, comprising:

(a) an isolated nucleic acid as given in one of the SEQ ID NoS. 130 to 191 or the complement of one of the SEQ ID Nos. 133 to 191; or (b) an isolated nucleic acid having at least 90% sequence identity with the DNA sequence as given in one of the SEQ ID Nos. 130 to 191 or the complement of one of the SEQ ID Nos. 130 to 191; or (c) an isolated nucleic acid specifically hybridising under stringent conditions with the DNA sequence as given in one of the SEQ ID Nos. 130 to 191 or the complement of one of the SEQ ID Nos. 130 to 191; or (d) an isolated nucleic acid as defined in any one of (a) to (c), which is interrupted by an intervening sequence; or (e) a fragment of any of the nucleic acids as defined in (a) to (d), which fragment is capable of driving and/or regulating expression.

The isolated promoter may further contain a minimal promoter, such as any known minimal promoter, e.g., ore of the minimal promoters described herein. In some embodiments, the isolated promoter further containing a minimal promoter contains:

(a) an isolated nucleic acid as given in one of the SEQ ID Nos. 5 to 66 or the complement of one of the SEQ ID Nos. 5 to 66; or (b) an isolated nucleic acid having at least 90% sequence identity with the DNA sequence as given in one of the SEQ ID Nos. 5 to 66 or the complement of one of the SEQ ID Nos. 5 to 66; or (c) an isolated nucleic acid specifically hybridising under stringent conditions with the DNA sequence as given in one of the SEQ ID Nos. 5 to 66 or the complement of one of the SEQ ID Nos. 5 to 66; or (d) an isolated nucleic acid as defined in any one of (a) to (c), which is interrupted by an intervening sequence; or (e) a fragment or any of the nucleic acids as defined in (a) to (d), which fragment is capable of driving and/or regulating expression.

The term “isolated” as used herein means being removed from its original source. Preferably, the “isolated” promoter is free of sequences such as protein encoding sequences or other sequences at the 3′ end) that naturally flank the promoter in the genomic DNA of the organism from which the promoter is derived. Further preferably, the “isolated” promoter is also free of sequences that naturally flank it at the 5′ end. Further preferably, the “isolated” promoter may comprise less than about 5 kb, 4 kb, 3 kb, 2 kb, 1.5 kb, 1.2 kb, 1 kb, 0.8 kb, 0.5 kb or 0.1 kb of nucleotide sequences that naturally occur with the promoter in genomic DNA from the organism of which the promoter is derived.

The present invention is not limited to the nucleic acids as presented by one of the SEQ ID Nos. 130 to 191, or SEQ ID No. 5 to 66. A person skilled in the art will recognize that variants or fragments of a nucleic acid may occur, whilst maintaining the same functionality. These variants or fragments may be man made (e.g. by genetic engineering) or may even occur in nature. Therefore the present invention extends to variant nucleic acids and fragments of one of the SEQ ID Nos. 130 to 191 or their complements, or SEQ ID Nos. 5 to 66 or their complements, which variants or fragments are useful in the methods of the present invention. Such variants and fragments include:

(a) an isolated nucleic acid as given in one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66 or the complement of one of the SEQ ID Nos. 130 to 191 or SEQ ID Nos. 5 to 66; or (b) an isolated nucleic acid having at least 90% sequence identity with any of the DNA sequences as given in one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, or the complement of one of the SEQ ID Nos. 130 to 191 or SEQ ID Nos. 5 to 66; or (c) an isolated nucleic acid specifically hybridizing under stringent conditions with any of the DNA sequences as given in one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, or the complement of one of the SEQ ID Nos. 130 to 191 or SEQ ID Nos. 5 to 66; or (d) an isolated nucleic acid as defined in any one of (a) to (c), which is interrupted by an intervening sequence; or (e) a fragment of any of the nucleic acids as defined in (a) to (d), which fragment is capable of driving and/or regulating expression.

Suitable variants of one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, encompass homologues which have in increasing order of preference at least 90%, 91%, 92%, 93%, 95%, 96%, 97%, 98% or 99% sequence identity with the nucleic acid acids as represented in one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66. The same is valid for the complements of the SEQ ID Nos. 130 to 191 or SEQ ID Nos. 5 to 66.

The percentage of identity may be calculated using an alignment program. Preferably a pair wise global alignment program may be used, which implements the algorithm of Needleman-Wunsch (J. Mol. Biol. 48: 443-453, 1970). This algorithm maximizes the number of matches and minimizes the number of gaps. Such programs are for example GAP, Needle (EMBOSS package), stretcher (EMBOSS package) or Align X (Vector NTI suite 5.5) and may use the standard parameters (for example gap opening penalty 15 and gap extension penalty 6.66). Alternatively, a local alignment program implementing the algorithm of Smith-Waterman may be used. Such programs are for example Water (EMBOSS package) or matcher (EMBOSS package). “Sequence identity” as used herein is preferably calculated over the entire length of the promoters as represented by one of the SEQ ID Nos. 130 to 191 or SEQ ID Nos. 5 to 66.

Search and identification of homologous nucleic acids, would be well within the realm of a person skilled in the art. Such methods, involve screening sequence databases with the sequences provided by the present invention, for example SEQ ID No. 5, preferably in a computer readable form. Useful sequence databases, include but are not limited to Genbank (http:/www.ncbi.nim.nih.gov/web/Genbank), the European Molecular Biology Laboratory Nucleic acid Database (EMBL) (http:/w.ebi.ac.uk/ebi-docs/embl-db.html) or versions thereof, or the MIPS database (http://mips.gsf.de/). Different search algorithms and software for the alignment and comparison of sequences are well known in the art. Such software includes, for example GAP, BESTFIT, BLAST, EASTA and TFASTA. Preferably BLAST software is used, which calculates percent sequence identity and performs a statistical analysis of the similarity between the sequences. The suite of programs referred to as BLAST programs has five different implementations: three designed for nucleotide sequence queries (BLASTN, BLASTX, and TBLASTX) and two designed for protein sequence queries (3LASTP and TBLASTN). The software for performing BLAST analysis is publicly available through the National Centre for Biotechnology Information. In case of SEQ ID No. 5 no homologues could be found using BLAST.

Examples of homologues having at least 90%, sequence identity with one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, are allelic variants of one of the SEQ ID Nos. 130 to 191, or of the SEQ ID Nos. 5 to 66. Allelic variants are variants of the same gene occurring in two different individuals of the same species and usually allelic variants differ by slight sequence changes. Allelic variants may encompass Single Nucleotide Polymorphisms (SNPs) as well as Small Insertion/Deletion Polymorphisms (INDELs). The size of INDELs is usually less than 100 bp. SNPs and INDELs form the largest set of sequence variants in naturally occurring polymorphic strains of most organisms. The same is valid for the complements of the SEQ ID Nos. 130 to 191 or SEQ ID Nos. 5 to 66.

Homologues suitable for use in the methods according to the invention may readily be isolated from their source organism via the technique of PCR or hybridization. Their capability of driving and/or regulating expression may readily be determined, for example, by following the methods described in the Examples section by simply substituting the sequence used in the actual example with the homologue.

Other suitable variants of one of the SEQ ID Nos. 130 to 191, or of one of SEQ ID Nos. 5 to 66 or their complements, encompassed by the present invention are nucleic acids specifically hybridising under stringent conditions to any one of the nucleic acids of one of the SEQ ID Nos. 130 to 191, or of the SEQ ID Nos. 5 to 66 or their complements. The term “hybridising” means annealing to substantially homologous complementary nucleotide sequences in a hybridization process. Tools in molecular biology relying on such a hybridization process include the polymerase chain reaction (PCR; and all methods based thereon), subtractive hybridisation, random primer extension, nuclease S1 mapping, primer extension, reverse transcription, cDNA synthesis, differential display of RNAs, and DNA sequence determination, Northern blotting (RNA blotting), Southern blotting (DNA blotting). The hybridisation process can also occur with one of the complementary nucleic acids immobilised to a matrix such as magnetic beads, Sepharose beads or any other resin. Tools in molecular biology relying on such a process include the isolation of poly (A+) mRNA. The hybridisation process can furthermore occur with one of the complementary nucleic acids immobilised to a solid support such as a nitro-cellulose or nylon membrane or immobilised by e.g. photolithography to, for example, a siliceous glass support (the latter known as nucleic acid arrays or microarrays or as nucleic acid chips). Tools in molecular biology relying on such a process include RNA and DNA gel blot analysis, colony hybridisation, plaque hybridisation, in situ hybridisation and microarray hybridisation. In order to allow hybridisation to occur, the nucleic acid molecules are generally thermally or chemically denatured to melt a double strand into two single strands and/or to remove hairpins or other secondary structures from single stranded nucleic acids. The stringency of hybridisation is influenced by conditions such as temperature, salt concentration and hybridisation buffer composition. Conventional hybridisation conditions are described in, for example, Sambrook 2001) Molecular Cloning: a laboratory manual, 3rd Edition Cold Spring Harbor Laboratory Press, CSH, New York, but the skilled craftsman will appreciate that numerous different hybridisation conditions can be designed in function of the known or the expected homology and/or length of the nucleic acid sequence. High stringency conditions for hybridisation include high temperature and/or low sodium/salt concentration (salts include sodium as for example in NaCl and Nacitrate) and/or the inclusion of formamide in the hybridisation buffer and/or lowering the concentration of compounds such as SDS (sodium dodecyl sulphate detergent) in the hybridisation buffer and/or exclusion of compounds such as dextran sulphate or polyethylene glycol (promoting molecular crowding) from the hybridisation buffer. Specifically hybridising under stringent conditions means that the sequences have to be very similar. Specific hybrisization under stringent conditions is preferably carried out at a temperature of 60° C. followed by washes in 0.1 to 1×SSC, 0.1×SDS, and 1×SSC, 0.1×SDS.

The invention also relates in certain embodiments to a nucleic acid molecule of at least 15 nucleotides in length hybridizing specifically with any of the nucleic acids of the invention, especially one of the SEQ ID Nos. 130 to 191 or their complement SEQ ID Nos. 192 to 253, or SEQ ID Nos. 5 to 66 or one of their complements SEQ ID Nos. 68 to 129. The invention also relates in some embodiments to a nucleic acid molecule of at least 15 nucleotides in length specifically amplifying a nucleic acid of the invention by polymerase chain reaction.

Another variant of any of one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, encompassed by the present invention are nucleic acids corresponding to one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66 or variants thereof as described hereinabove, which are interrupted by an intervening sequence. For example, any of the nucleic acids as presented in one of the SEQ ID Nos. 130 to 191, or of the SEQ ID Nos. 5 to 66, may be interrupted by an intervening sequence. With “intervening sequences” is meant any nucleic acid or nucleotide, which disrupts another sequence. Examples of intervening sequences comprise introns, nucleic acid tags, T-DNA and mobilizable nucleic acids sequences such as transposons or nucleic acids that can be mobilized via recombination. Examples of particular transposons comprise Ac (activator), Ds (Dissociation), Spm (suppressor-Mutator) or En. The introduction of introns into promoters is now widely applied. The methods according to the present invention may also be practised using a nucleic acid sequence according to one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, provided with an intron. In case the intervening sequence is an intron, alternative splice variants of the nucleic acids according to the invention may arise. The term “alternative splice variant” as used herein encompasses variants of a nucleic acid sequence in which intervening introns have been excised, replaced or added. Such splice variants may be found in nature or may be manmade. Methods for making such promoters with an intron or for making the corresponding splice variants are well known in the art.

Variants interrupted by an intervening sequence, suitable for use in the methods according to the invention may readily be determined for example by following the methods described in the examples section by simply substituting the sequence used in the actual example with the variant.

The variant nucleic acids as described hereinabove may be found in nature (for example allelic variants or splice variants). Additionally and/or alternatively, variants of one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, as described hereinabove may be manmade via techniques well known in the art involving for example mutation, substitution, insertion, deletions or derivation. The present invention also encompasses such variants, as well as their use in the methods of the present invention.

A “mutation variant” of a nucleic acid may readily be made using recombinant DNA manipulation techniques or nucleotide synthesis. Examples of such techniques include site directed mutagenesis via M13 mutagenesis, T7-Gen in vitro mutagenesis (USD, Cleveland, Ohio), QuickChange Site Directed mutagenesis (Stratagene, San Diego, Calif.), PCR-mediated site-directed mutagenesis or other site-directed mutagenesis protocols. Alternatively, the nucleic acid of the present invention may be randomly mutated.

A “substitutional variant” refers to those variants in which at least one residue in the nucleic acid sequence has been removed and a different residue inserted in its place. Nucleic acid substitutions are typically of single residues, but may be clustered depending upon functional constraints placed upon the nucleic acid sequence; insertions usually are of the order of about 1 to about 10 nucleic acid residues, and deletions can range from about 1 to about 20 residues.

An “insertional variant” of a nucleic acid is a variant in which one or more nucleic acid residues are introduced into a predetermined site in that nucleic acid. Insertions may comprise 5′-terminal and/or 3′-terminal fusions as well as intrasequence insertions of single or multiple nucleotides. Generally, insertions within the nucleic acid sequence will be smaller than 5′- or 3′-terminal fusions, of the order of about 1 to 10 residues. Examples of 5′- or 3′-terminal fusions include the coding sequences of binding domains or activation domains of a transcriptional activator as used in the yeast two-hybrid system or yeast one-hybrid system, or of phage coat proteins, (histidine)₆-tag, glutathione S-transferase-tag, protein A, maltose-binding protein, dihydrofolate reductase, Tag 100 epitope, c-myc epitope, FLAG®-epitope, lacZ, CMP (calmodulin-binding peptide), HA epitope, protein C epitope and VSV epitope.

The term “derivative” of a nucleic acid may comprise substitutions, and/or deletions and/or additions of naturally and non-naturally occurring nucleic acid residues compared to the natural nucleic acid. Derivatives may, for example, comprise methylated nucleotides, or artificial nucleotides.

Also encompassed within the present invention are promoters, comprising a fragment of any of the nucleic acids as presented by one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, or variants thereof as described hereinabove. A “fragment” as used herein means a portion of a nucleic acid sequence. Suitable fragments useful in the methods of the present invention are functional fragments, which retain at least one of the functional parts of the promoter and hence are still capable of driving and/or regulating expression. Examples of functional fragments of a promoter include the minimal promoter, the upstream regulatory elements, or any combination thereof.

Suitable fragments may range from at least about 20 base pairs or about 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 600, 850, 900, 950 or 1000 base pairs, up to about the full length sequence of the invention. These base pairs are typically immediately upstream of the transcription initiation start, but alternatively may be from anywhere in the promoter sequence.

Suitable fragments useful in the methods of the present invention may be tested for their capability of driving and/or regulating expression by standard techniques well known to the skilled person, or by the following method described in the Example section.

The term “promoter” as used herein is taken in a broad context and refers to regulatory nucleic acid sequences capable of effecting (driving and/or regulating) expression of the sequences to which they are operably linked. A “promoter” encompasses transcriptional regulatory sequences derived from a classical genomic gene. Usually a promoter comprises a TATA box, which is capable of directing the transcription initiation complex to the appropriate transcription initiation start site. However, some promoters do not have a TATA box (TATA-less promoters), but are still fully functional for driving and/or regulating expression. A promoter may additionally comprise a CCAAT box sequence and additional regulatory elements (i.e. upstream activating sequences or cis-elements such as enhancers and silencers).

“Driving expression” as used herein means promoting the transcription of a nucleic acid.

“Regulating expression” as used herein means influencing the level, time or place of transcription of a nucleic acid. The promoters of the present invention may thus be used to increase, decrease or change in time and/or place transcription of a nucleic acid. For example, they may be used to limit the transcription to certain cell types, tissues or organs, or during a certain period of time, or in response to certain environmental conditions.

According to a particular embodiment, the invention provides an isolated promoter as mentioned hereinabove, which is a hybrid promoter. The term “hybrid promoter” as used herein refers to a chimeric promoter made, for example, synthetically, for example by genetic engineering. Preferred hybrid promoters according to the present invention comprise a part, preferably a functional part, of one of the promoters according to the present invention and at least another part, preferably a functional part of a promoter. The latter part may be a part of any promoter, including any one of the promoters according to the present invention and other promoters. One example of a hybrid promoter comprises regulatory element(s) of a promoter according to the present invention combined with the minimal promoter of another promoter. Another example of a hybrid promoter is a promoter comprising additional regulatory elements to further enhance its activity and/or to alter its spatial and/or temporal expression pattern.

The present invention also provides in some aspects use of a functional fragment of one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, or variant thereof for changing the expression pattern of a promoter. In such methods, at least part of the nucleic acid of one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, according to the present invention is combined with at least one fragment of another promoter.

Further, the invention provides a genetic construct comprising:

(a) an isolated promoter as defined hereinabove (b) a heterologous nucleic acid sequence operably linked to isolated promoter of (a), and optionally (c) a 3′ transcription terminator

The term “genetic construct” as used herein means a nucleic acid made by genetic engineering.

The term “operably linked” to a promoter as used herein means that the transcription is driven and/or regulated by that promoter. A person skilled in the art will understand that being operably linked to a promoter preferably means that the promoter is positioned upstream (i.e. at the 5′-end) of the operably linked nucleic acid. The distance to the operably linked nucleic acid may be variable, as long as the promoter of the present invention is capable of driving and/or regulating the transcription of the operably linked nucleic acid. For example, between the promoter and the operably linked nucleic acid, there might be a cloning site, an adaptor, and/or a transcription or translation enhancer.

The operably linked nucleic acid may be any coding or non-coding nucleic acid. The operably linked nucleic acid may be in the sense or in the anti-sense direction. Typically in the case of genetic engineering of host cells, the operably linked nucleic acid is to be introduced into the host cell and is intended to change the phenotype of the host cell. Alternatively, the operably linked nucleic acid is an endogenous nucleic acid from the host cell.

The term “heterologous” as used herein is intended to be “heterologous to a promoter of the present invention”. A nucleic acid that is heterologous to a promoter of the present invention is not naturally occurring in the nucleic acid sequences flanking the promoter of the present invention when it is in its biological genomic environment. While the nucleic acid may be heterologous to a promoter of the present invention, it may be homologous or native or heterologous or foreign to the host cell. The heterologous operably linked nucleic acid may be any nucleic acid (for example encoding any protein), provided that it comprises or it is flanked by at least one nucleotide which is normally not flanking the promoter of the present invention.

The term “transcription terminator” as used in (c) refers to a DNA sequence at the end of a transcriptional unit which signals termination of transcription. Terminators are 3′-non-translated DNA sequences usually containing a polyadenylation signal, which facilitates the addition of polyadenylate sequences to the 3′-end of a primary transcript. Terminators active in and/or isolated from viruses, yeasts, moulds, bacteria, insects, birds, mammals and plants are known and have been described in literature. Examples of terminators suitable for use in the genetic constructs of the present invention include any eukaryotic terminator or viral terminator, e.g. Bovine Growth Hormone poly A or SV40 poly A. These polyadenylation signals are known in the art.

Furthermore, the present invention encompasses a host cell comprising an isolated promoter or a genetic construct according to the invention as described hereinabove. In particular embodiments of the invention, the host cell is selected from bacteria, algae, fungi, yeast, plants, insect or animal host cells.

In a preferred embodiment the host cell is a cell in a disease state, preferably a cancer cell.

The invention further provides a method for driving and/or regulating expression of a nucleic acid in a cell, comprising:

(a) operably linking a nucleic acid to an isolated nucleic acid according to the invention as described hereinabove, such as to one of the SEQ ID Nos. 130 to 191, or SEQ ID Nos. 5 to 66, or a variant or fragment thereof, and (b) introducing the resultant genetic construct into a cell, preferably a cell in a disease state, more preferably a cancer cell.

Preferably the operably linked nucleic acid of (a) is heterologous to the nucleic acids according to the present invention.

This method may further comprise cultivating the transformed cell under conditions promoting growth, promoting regeneration and/or promoting maturation.

Furthermore, the expression of the operably linked nucleic acid may be driven and/or regulated in particular cells, tissues or organs of an organism, preferably a mammal. Accordingly, the invention in some embodiments provides a method as described above, wherein the expression is constitutive expression or tissue-specific expression. For these embodiments, reference is made to the example section where the specific expression patterns of the promoters according to the invention are described and where different types of tissue-specific expression are detailed.

The present invention further encompasses the use of an isolated nucleic acid as defined hereinabove to drive and/or regulate expression of an operably linked nucleic acid.

BRIEF DESCRIPTION OF THF DRAWINGS

Other objects and advantages of the present invention may be ascertained from a reading of the specification and appended claims in conjunction with the drawings therein.

For a more complete understanding of the present invention, reference is established to the following description made in connection with accompanying drawings in which:

FIG. 1 Schematic representation of the method of the invention;

FIG. 2 Vector diagram of the vector pSmoothy. The sequence of this vector is SEQ ID No. 4;

FIGS. 3A-B FIG. 3A shows efficacy of GFP expression in different cell lines transfected with vector. FIG. 3B shows fluorescence sorting data of HT29 cells top) HT29; (middle) HT29-SYN pre-sort; bottom HT29-SYN post-sort).

FIG. 4 Agarose gel of the PCR product amplified with the primers SYN1S and SYN1AS. The different lanes show L1: pSmoothy retroviral library 1 prior to transduction into CRC cells; L2: pSmoothy retroviral library 2 prior to transduction into CRC cells; HT29: control; S1: first sort of HT29-transduced cells; S2: second sort of HT29-transduced cells;

FIG. 5A Level of LacZ gene expression that was achieved in colorectal cancer cells (average of HT29, DLD-1, HCT116 and RKO cells; upper column for each promoter; light gray) versus HELA control cells (lower column for each promoter; dark gray) from each of the 140 potential synthetic promoters (normalised to the level of expression obtained with the pCMV-beta control plasmid).

FIG. 5B LacZ expression of HT29 and NEURO2A cells transfected with different promoters (CMV-beta; Mucin-1; CRCSE SEQ ID No. 5).

FIG. 6A FACS analysis demonstrating the proportion of cells expressing GFP from a promoter with average activity in control HELA cells and CRC Cell lines; HCT116, HT29, DLD1 and RKO. GFP cells are present in the R2 gate.

FIG. 6B Activity measurements on different promoters generated by an embodiment of the method of the invention.

FIG. 1 shows a flow diagram of one example of the method for making and selecting a transcription enhancing combined promoter cassette.

In this exemplary method, in a first step (10) a plurality of transcription factor regulatory elements that are associated with any of a plurality of genes whose expression is identified as being aberrantly regulated in cancer cells isolated from a number of different sources is provided.

In a second step (12) of this exemplary method, said transcription factor regulatory elements are selected according to a number of pre-defined criteria. In this example, as a first criterion said transcription factor regulatory elements have to be in close proximity to more than fifty percent of the genes found to be aberrantly regulated in cancer cells. As a second criterion in this example, the frequency of occurrence and as a third criterion is the length in nucleotides of the said transcription factor regulatory elements are both used to identify candidate regulatory elements according to the following relationship: frequency^((1/length)), with frequency defined as above.

In a preferred embodiment, the candidate regulatory elements in the case of colon cancer are the sequences listed in table 3.

In the next step (14) of this exemplary method, a library of randomly combined transcription factor regulatory elements selected in step (12) is constructed.

In the next step (16) of this exemplary method, the combined transcription factor regulatory elements are inserted upstream of a minimum promoter followed by a reporter gene in a vector. Preferably one combined transcription factor regulatory element is inserted in each vector.

In the next step (18) of this exemplary method, the vector is inserted into a host cells.

In the next step (20) the cells are screened for cells showing enhanced expression of the reporter gene, and identify the cells comprising the combined promoter cassette from the library.

EXAMPLE 1. Selection of Genes Upregulated in Colorectal Cancer

Table 2 represents a selection of genes identified by a meta-analysis of microarray data from colon cancer sources from a study conducted by Rhodes et al (Rhodes et al (2004) PNAS 2004; 101; 9309-14). This resulted in the identification of the 17 genes listed in table 2 shown to be upregulated in colorectal cancer biopsies.

These genes were then screened to ensure that overexpression was a result of altered transcription factor activation, instead of chromosomal amplification, in order to select cis-regulatory elements that will be active in the context of an altered transcription factor environment. This resulted in the exclusion of three genes: TOP2A, SMARCA4 and TRAF4 (indicated by *).

Further the literature was searched using pubmed in order to find genes whose overexpression in colorectal cancer had previously been shown by independent methods. Depending on the expression levels and assays used for detection, genes were scored as ‘+++’; Substantial evidence to support their overexpression, ‘++’; Significant evidence to support their overexpression, and ‘+’; Evidence to support their overexpression.

Due to improved computing power, an aim of the invention is to analyse all regulatory sequences of all differentially regulated genes. Therefore this selection step is only optionally.

Genes, where no further evidence regarding their overexpression in colorectal cancer was found, were excluded. Finally, the regulatory regions of the following seven genes with a view to select cis-regulatory elements to form a synthetic promoter active specifically in colon cancer cells were examined: PLK, G3BP, E2-EPF, MMP9, MCM3, PRDX4 and CDC2.

2. Identification of Regulatory Elements from Upregulated Genes

Upon deciding on the genes upregulated in colorectal cancer, the nucleotide sequence of each gene (a total of seven genes) was obtained with 5 kb upstream/downstream from UCSC Golden-Path (www.genome.ucsc.edu) with the use of the UCSC Genome Browser on Human March 2006 Assembly (http://genome.ucsc.edu/cgi-bin/hgTracks?org=human). Using the BIOBASE Biological Databases (www.generegulation.com), each retrieved sequence was BLASTed against the TRANSFAC Factor Table by using the BLASTX search tool (version 2.0.13) of the TFBLAST program (www.generegulation.com/cgi-bin/pub/programs/tfblast/tfblast.cgi) for searches against nucleotide sequences in order to identify regulatory elements. The selection of regulatory elements was based on sequence homology with significantly high (0.7-1.0) corresponding consensus sequences (identity threshold), while no restriction on score or length threshold was imposed. The BLAST results for the genes of interest were cross-referenced in order to obtain common regulatory element lists with significant e-values (<1e-03) as well as belonging to the species of choice (Homo Sapiens). Upon further review, the colon cancer gene list showed good evidence of regulatory elements since (a) significant e-values were present in all seven genes (b) multiple common regulatory elements were present in all seven genes, (c) the majority of genes present in the colon cancer gene list are also present in other cancer gene lists (data not shown), and (d) substantial/significant evidence to support the genes overexpression were established from expression levels and assays used for detection.

The seven gene sequences of interest from the colon cancer gene list were further investigated with the use of the PATCH public 1.0 (Pattern Search for Transcription Factor Binding Sites) (http://www.gene-regulation.com/cgi-bin/pub/programs/patch/bin/patch.cgi), from the BIOBASE Biological Databases. The search was conducted for all sites with a minimum site length of 7 bases, maximum number of mismatches of 0, mismatch penalty of 100, and lower score boundary of 100. The results of all seven gene sequences were further analyzed by grouping them all together, excluding all transcription factor binding sites except Homo sapiens.

It was then proceeded to examine the frequency that each transcription factor binding site occurred in close proximity to the seven genes that were originally identified as being upregulated in colon cancer cells. In some cases one sequence was present multiple times in proximity to a single gene under evaluation. Thus, in order to determine the frequency of occurrence of a transcription factor binding site; the sum of each time a binding site was detected in all genes was calculated and then used the sum of all binding sites present in all genes as the common denominator.

3. Selection of Regulatory Elements for Introduction into Screening Library

A total of 328 cis-regulatory sequences were identified that were present 5854 times in the seven gene sequences that were identified as being upregulated in colorectal cancer. Then those cis-regulatory sequences were identified, which were present at the highest proportion and which displayed the highest level of conservation between genes.

To accomplish this, sequences were selected for library construction according to the following two criteria:

-   -   A: They were present n four or more of the seven genes         identified by the gene expression profile screen, i.e. present         in the regulatory regions of more than fifty percent of the         candidate genes.     -   B: The cis-regulatory sequences that were present at the highest         frequency in gene regulatory regions were then subsequently         analyzed using the following selection criterion (SYN value):

(frequency of cis-sequence)^((1/length of cis sequence in bp))>0.5

The SYN value selection criterion has the advantage to take into account that longer sequences, which may be present at lower frequencies, may actually represent a higher degree of conservation and may therefore by important in specifically driving gene expression in colon cancer cells.

The ten cis regulatory sequences (listed in table 3) with the highest SYN value were then synthesized and used to create a retroviral vector library for selection of synthetic promoters in a colorectal cancer cell line.

4. Construction of the Retroviral Screening Library and Screening in Colon Cancer Cells

In order to select the promoters with the optimal activity in colorectal cancer cells a similar protocol was used to that described by Edelman et al (2000) [PNAS 97 (7), 3038-43]. In brief, sense and antisense oligonucleotides corresponding to the ten selected cis elements were designed to contain a TCGA 5′ overhang after annealing. Annealed oligonucleotides were then randomly ligated together using T4 ligase and ligated oligonucleotides in the range of 0.3-1.0 kb were selected for by extraction from a 1.0% agarose gel. IL is also possible to use Gateway cloning techniques. These randomly ligated oligonucleotides were then subsequently ligated to the retroviral library pSmoothy vector (FIG. 2; SEQ-ID No. 4), which had been treated with Xho I restriction enzyme and library complexity was measured by transforming 1/50th of the ligation reaction in supercompetent Top10 bacteria using an electroporator. Plasmid DNA from pSmoothy libraries with a complexity greater than 104 colonies was then expanded and used to create retroviral vectors.

pSmoothy was constructed in order to select potential synthetic promoter sequences by their ability to express both GFP and neomycin in target cells (FIG. 2). It was constructed as a self-inactivating (SIN) retroviral vector so that upon integration into the genome of transduced cells its 3′-UTR can no longer act as a promoter. The vector comprises the mucin minimal promoter which is located within the proviral genome and immediately downstream of the polylinker, where randomly ligated oligonucleotides are inserted. GFP and neomycin coding sequences are located immediately downstream of the minimal promoter and it is expression of these two genes which is used to select the potential synthetic promoter sequences with optimal activity. The sequence of pSmoothy-1 is shown in SEQ-ID No. 4.

Retroviral vectors were constructed by transfecting the pSmoothy library with a retroviral VSV-G envelop construct into 293 cells stably expressing Gag and Pol and allowing viral vector to be produced over a period of 40 hours. This retroviral vector library was then used to transduce HT29, DLD-1, HCT-116 and RKO colorectal cancer cells at various titers and the transduced cells were subjected to selection with 1 mg/ml G418 for a period of several weeks. FIG. 3A demonstrates efficacy of GFP expression in each cell line. The colorectal cancer cells expressing the highest amounts of GFP were then sorted using a FACS Aria cell sorter (3D) by selecting toe cells expressing the highest amount of GFP. This sorted population was then subject to further selection with 1 mg/ml G418 and then sorted a second time, again selecting the 10% cells expressing the highest amount of GFP (FIG. 3B; (a) HT29; (b) HT29-SYN pre-sort; (c) HT29-SYN post-sort). Genomic DNA was then prepared from sorted colorectal cancer cells and promoter sequences were rescued by PCP using the following primers that specifically hybridize to the pSmoothy vector:

SYN1S SEQ-ID No. 2: 5′-TAT CTG CAG TAG GCG CCG GAA TTC-3′ SYN1AS SEQ-ID No. 3: 5′-GCA ATC CAT GGT GGT GGT GAA ATG-3′

FIG. 4 shows a typical PCR from the genomic DNA of retrovirally-transduced HT29 cells using these primers, where amplification of several species occurs after the first sort (S1) with the FACS Aria. After the second sort (S2) a single product at 290 bp was amplified.

This process was then repeated using genomic DNA isolated from pSmoothy-transduced DLD-1, HCT-116 and RKO cell lines and isolated a total of 250 sequences with the potential to drive gene expression specifically in colorectal cancer cells.

Then the ability of the 140 potential colon cancer-specific synthetic enhancer elements (CRCSE) to drive expression of the LacZ reporter gene was evaluated in all colorectal cancer cell lines under investigation: HT29, DLD1, RKO and HCT116 cells. 24 synthetic promoter elements were identified that were broadly able to drive a varying degree of LacZ expression across the tour different colorectal cancer cell lines; ten of which were deemed to drive high expression and were chosen for further analysis. FIG. 5A shows the level of LacZ gene expression that was achieved in colorectal cancer cells (average of HT29, DLD-1, HCT-116 and RKO cells) versus HELA control cells from each of the 140 potential synthetic promoters (normalised to the level of expression obtained with the pCMV-beta control plasmid). From these cell lines 5 lines showing activity by two independent means of testing, i.e. beta-galactosidase and staining of cells were selected. They correspond to numbers 001, 102, 103, 105, 106, 108 in FIG. 5A. The corresponding SEQ-IDs are shown in table 5. The sequences of the promoters without the mucin-1 minimum promoter are given in the SEQ-ID Nos. 130 to 135 for the sense and SEQ-ID Nos. 192 to 197 for the antisense strands.

Overall the results illustrated that the synthetic promoters constructed in this study only drive efficient gene expression in cell lines derived from patients with colorectal cancer. Specifically, high levels of beta-galactosidase expression was detected in HT29, RKO, HCT116, Dld-1 and Caco-2 cells, and minimal levels of gene expression was detected in Hela, Neuro2A, MCF-7, Panc-1, CV-1 and 3T3 cells. The results were further compared with cells transfected with vectors pCMV-beta (CMV promoter) and pDRIVE-Muc1 (Mucin-1 promoter; Invitrogen).

The results from one synthetic promoter CRCSE-1 (SEQ-ID No. 5, anti-sense strand SEQ-ID No. 11) are summarised in table 4 ((+++) high expression, (++) medium expression, (+) low expression, (+/−) very low expression, (−) no expression. These results clearly demonstrate that the selection procedure outlined in this example is capable of generating synthetic promoters with specific activity in colon cancer cells.

FIG. 5B shows representative expression levels of Lac Z mediated by CRCSE-1 (SED-ID No. 5; anti-sense strand SEQ-ID No. 68) in HT29 and Neuro2A cells transfected using Lipofectamine 2000 and stained for LacZ expression 48 hours post-transfection. Notably, control cell lines, including NEURO2A, NIH3T3, CV1, HELA and COS-7 cells, did not exhibit any expression of Lac Z when transfected with CRCSE-1 (table 4).

Within these sequences the following TFES could be identified using 86% homology as criteria. In total all the sequences used show a homology of approx. 72%. Table 5 shows the identified elements. The mutation was most likely introduced during the Neomycin selection procedure. Since the minimum promoter is an essential binding site there are less mutations within this region of each sequence.

It then was assessed whether the number of cis-elements present in each promoter is an important indicator of promoter strength and specificity. A process was carried out to select promoter sequences with a higher degree of stringency; i.e. to select promoters containing cis-elements with 100% homology to the input oligonucleotides. A further 82 sequences thus were subcloned from the promoter library isolated from CRC cell genomic DNA (described above) into pBluescript II KSS; the sequences of each clone were analysed prior to expression analysis. From these 82 sequences 55 were identified containing cis-regulatory elements with 100% homology to input oligonucleotides (SED-ID No. 11 to SEQ-ID No. 66; antisense strands SEQ-ID No. 68 to SEQ-ID No. 129, Tables 6 and 7; All these sequences comprise a Mucin-1 minimum promoter. The sequences without this promoter are also given in table 6). As controls, sequences were sub-cloned from the random ligation products of all ten cis-regulatory elements prior to selection in CRC cell lines. The results showed that on average, only 2.2 cis-regulatory elements per sequence were found in unselected sequences, compared to 4.0 elements per promoter subjected to selection through the CRC cell lines (p<0.001; Mann-Whitney non-parametric test). Indeed, only 3/22 sequences in the control group contained four or more cis-regulatory elements, compared to over 31/55 promoters containing four or more cis-elements from the group subjected to selection. Moreover, cis-elements with a SYN value greater than 0.6 represented 70.0% of all the elements in the 55 identified promoters, thus confirming the importance of the SYN selection formula. To correlate the presence of specific cis-regulatory elements to level and specificity of expression, 28/31 promoters were inserted into the pSmoothy retroviral vector and their ability to drive GFP expression in CRC cells compared to the HELA control cell line was monitored.

Efficiency of GFP expression was determined by FACS analysis and the proportion of cells fluorescing above a threshold value of 200 units on the FL1 channel was determined for all promoters. Depending on the cell line, an average 1.0-10.0% of the cells expressing OFF demonstrated fluorescence above this level. All promoters analysed generated significantly higher levels of expression in CRC cell lines (HCT116, HT29, DLD1 and RKO) when compared to the HELA control cell line; where only a small proportion of cells were GFP positive. This is illustrated in FIG. 6A, which comprises the FACS results from promoter 230; a synthetic promoter that expressed GFP to average levels in all CRC cell lines; see FIG. 6B. To identify which promoters were the most efficient, an expression ratio for each promoter in all cell lines was determined; this expression ratio was defined as the proportion of cells expressing GFP above the threshold value for each individual promoter divided by the average proportion above the threshold for all promoters. The results of this analysis are shown in figure GB, which illustrates that promoters 239, 213, 215, 248 and 254 show the highest activity in all CRC cell lines compared to the other promoters.

We further examined which cis-elements constituted these more efficient promoters and found that on average the five cis-elements with the highest SYN value represented 64% of all the regulatory elements in each promoter. Thus further demonstrating the importance of the SYN value for selecting the optimal elements to maximise efficient and selective expression.

Taken together the results demonstrate that the SYN selection formula and the methods provided herein represent a useful tool in selecting cis-regulatory elements (i.e., TFREs) for inclusion in synthetic promoter libraries. Several promoters were constructed using the described methodology that could efficiently express GFP or Lac Z specifically in CRC cell lines, whilst showing no or limited activity in control cells. It is proposed that this method can be applied in the construction of any eukaryotic promoter designed to be active in specific environmental or diseased conditions.

While the present inventions have been described and illustrated in conjunction with a number of specific embodiments, those skilled in the art will appreciate that variations and modifications may be made without departing from the principles of the inventions as herein illustrated, as described and claimed. The present inventions may be embodied in other specific forms without departing from their spirit or essential characteristics. The described embodiments are considered in all respects to be illustrative and not restrictive. The scope of the inventions is, therefore, indicated by the appended claims, rather than by the foregoing description. All changes which come within the meaning and range of equivalence of the claims are to be embraced within their scope.

REFERENCES CITED

-   Rhodes, D. R. et al. Mining for regulatory programs in the cancer     transcriptome. Nat. Genet. 37, 579-583 (2005); -   Segal, F., Friedman, N., Koller, D., & Regev, A. A module map     showing conditional activity of expression modules in cancer. Nat.     Genet. 36, 1090-1098 (2004); -   Segal, E. et al. Module networks: identifying regulatory modules and     their condition-specific regulators from gene expression data. Nat.     Genet. 34, 166-176 (2003); -   Wingender, E. Compilation of transcription regulating proteins.     Nucleic Acids Res 16, 1879-1902 (1988); -   Kel-Margoulis, O. V., Kel, A. E., Reuter, I., Deineko, I. V., &     Wingender, E. TRANSCompel: a database on composite regulatory     elements in eukaryotic genes. Nucleic Acids Res 30, 332-334 (2002); -   Blanco, E., Farre, D., Alba, M. M., Messeguer, X., & Guigo, R. ABS:     a database of Annotated regulatory Binding Sites from orthologous     promoters. Nucleic Acids Res 34, D63-D67 (2006); -   Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W. W., &     Lenhard, B. JASPAR: an open-access database for eukaryotic     transcription factor binding profiles. Nucleic Acids Res 32, D91-D94     (2004); -   Jagannathan, V., Roulet, E., Delorenzi, M., & Bucher, P. HTPSELEX—a     database of high-throughput SELEX libraries for transcription factor     binding sites. Nucleic Acids Res 34, D90-D94 (2006); -   Matys, V. et al. TRANSFAC: transcriptional regulation, from patterns     to profiles. Nucleic Acids Res 31, 374-378 (2003); -   Zhao, F., Xuan, Z., Liu, L., & Zhang, M. Q. TRED: a Transcriptional     Regulatory Element Database and a platform for in silico gene     regulation studies. Nucleic Acids Res 33, D133-D107 (2005); -   Sinha, S. & Tomua, M. YMF: A program for discovery of novel     transcription factor binding sites by statistical     overrepresentation. Nucleic Acids Res 31, 3586-3588 (2003); -   Sinha, S. & Tomoa, M. Discovery of novel transcription factor     binding sites by statistical overrepresentation. Nucleic Acids Res     30, 5549-5560 (2002); -   Rebeiz, M., Reeves, N. L., & Posakony, J. W. SCORE: a computational     approach to the identification of cis-regulatory modules and target     genes in whole-genome sequence data. Site clustering over random     expectation. Proc. Natl. Acad. Sci. U.S.A. 99, 9888-9893 (2002); -   Suzuki, Y., Yamashita, R., Sugano, S., & Nakai, K. DBTSS, DataBase     of Transcriptional Start Sites: progress report 2304. Nucleic Acids     Res 32, D78-D81 (2004); -   Suzuki, Y., Yamashita, R., Naka, K., & Sugano, S. DBTSS: DataBase of     human Transcriptional Start Sites and full-length cDNAs. Nucleic     Acids Res 30, 328-331 (2002); -   Davuluri, R. V., Grosse, I., & Zhang, M. Q. Computational     identification of promoters and first exons in the human genome.     Nat. Genet. 29, 412-417 (2001); -   Roth, F. P., Hughes, J. D., Estep, P. W., & Church, G. M. Finding     DNA regulatory motifs within unaligned noncoding sequences clustered     by whole-genome mRNA quantitation. Nat. Biotechnol. 16, 939-945     (1998); -   Bussemaker, H. J., Li, H., & Siggia, E. D. Regulatory element     detection using correlation with expression. Nat. Genet. 27, 167-171     (2001); -   Jensen, L. J. & Knudsen, S. Automatic discovery of regulatory     patterns in promoter regions based on whole cell expression data and     functional annotation. Bioinformatics. 16, 326-333 (2000); -   Jegga, A. G. et al. Detection and visualization of compositionally     similar cis-regulatory element clusters in orthologous and     coordinately controlled genes. Genome Res 12, 1408-1417 (2002); -   Dieterich, C., Wang, H., Rateitschak, K., Luz, H., & Vingron, M.     CORG: a database for Comparative Regulatory Genomics. Nucleic Acids     Res 31, 55-57 (2003; -   Lenhard, B. et al. Identification of conserved regulatory elements     by comparative genome analysis. J. Biol. 2, 13 (2003); -   Karanam, S. & Moreno, C. S. CONFAC: automated application of     comparative genomic promoter analysis to DNA microarray datasets.     Nucleic Acids Res 32, W475-W484 (2004); -   La Rosa, P. et al. VAMP: visualization and analysis of array-CGH,     transcriptome and other molecular profiles. Bioinformatics. 22,     2066-2073 (2006); -   Jegga, A. G. et al. CisMols Analyzer: identification of     compositionally similar cis-element clusters in ortholog conserved     regions of coordinately expressed genes. Nucleic Acids Res 33,     W408-W411 (2005); -   Blanchette, M. et al. Genome-wide computational prediction of     transcriptional regulatory modules reveals new insights into human     gene expression. Genome Res 16, 656-668 (2006); -   Ferretti, V. et al. PReMod: a database of genome-wide mammalian     cis-regulatory module predictions. Nucleic Acids Res 35, D122-D126     (2007); -   Sharov, A. A., Dudekula, D. B., & Ko, M. S. CisView: a browser and     database of cis-regulatory nodules predicted in the mouse genome.     DNA Res 13, 123-134 (2006); -   Vega, V. B., Bangarusany, D. K., Miller, L. D., Liu, E. T., &     Lin, C. Y. BEARR: Batch Extraction and Analysis of cis-Regulatory     Regions. Nucleic Acids Res 32, W257-W260 (2004); -   Dubchak, I. & Ryaboy, D. V. VISTA family of computational tools for     comparative analysis of DNA sequences and whole genomes. Methods Mol     Biol. 338, 69-89 (2006); -   Lardenois, A. et al. PromAn: an integrated knowledge-based web     server dedicated to promoter analysis. Nucleic Acids Res 34,     W578-W583 (2006); -   Liu, C. C. et al. CRSD: a comprehensive web server for composite     regulatory signature discovery. Nucleic Acids Res 34, W571-W577     (2006); -   Sun, H. et al. MPromDb: an integrated resource for annotation and     visualization of mammalian gene promoters and ChIP-chip experimental     data. Nucleic Acids Res 34, D98-103 (2006); -   Li, X., Eastman, E. M., Schwartz, R. J., & Draghia-Akli, R.     Synthetic muscle promoters: activities exceeding naturally occurring     regulatory sequences. Nat. Biotechnol. 17, 241-245 (1999); -   Dai, C., McAninch, R. E., & Sutton, R. E. Identification of     synthetic endothelial cell-specific promoters by use of a     high-throughput screen. J. Virol. 78, 6209-6221 (2004); -   Needleman-Wunsch, J. Mol. Biol. 48, 443-453, (1970); -   Sambrook (2001) Molecular Cloning: a laboratory manual, 3rd Edition     Cold Spring Harbor Laboratory Press, CSH, New York -   Rhodes et al (2004) PNAS 2004; 101; 9309-14; -   Edelman et al (2000) [PNAS 97 (7), 3036-43;

TABLE 1 Resource Description Citation DBTSS Database of Suzuki, Y., Yamashita, R., transcriptional Nakai, K., & Sugano, S. start sites DBTSS: DataBase of human Transcriptional Start Sites and full-length cDNAs. Nucleic Acids Res 30, 328-331 (2002). TRAFAC Conserved cis- Jegga, A. G. et al. Detection element search and visualization of tool compositionally similar cis- regulatory element clusters in orthologous and coordinately controlled genes. Genome Res 12, 1408-1417 (2002). TRANSCompel Database of Kel-Marqoulis, O. V., Kel, A. composite E., Reuter, I., Deineko, I. regulatory V., & Wingender, elements E. TRANSCompel: a database on composite regulatory elements in eukaryotic genes. Nucleic Acids Res 30, 332-334 2002). TRANSFAC Eukaryotic Matys, V. et al. TRANSFAC: transcription transcriptional regulation, factor database from patterns to profiles. Nucleic Acids Res 31, 374-378 (2003). Phylofoot Tools for Lenhard, B. et al. Identification phylogenetic of conserved regulatory footprinting elements by comparative purposes genome analysis. J.Biol. 2, 13 (2003). CORG Multi-species Dieterich, C., Wang, H., DNA comparison Rateitschak, K., Luz, H., & and annotation Vingron, M. CORG: a database for Comparative Regulatory Genomics. Nucleic Acids Res 31, 55-57 (2003). CONSITE Explores trans- Lenhard, B. et al. Identification factor binding of conserved regulatory sites from two elements by comparative species genome analysis. J.Biol. 2, 13 (2003). CONFAC Conserved Karanam, S. & Moreno, C. S. transcription CONFAC: automated factor binding application of comparative site finder genomic promoter analysis to DNA microarray datasets. Nucleic Acids Res 32, W475- W484 (2004). CisMols Identifies Jegga, A. G. et al. CisMols cis-regulatory Analyzer: identification of modules from compositionally similar inputed data cis-element clusters in ortholog conserved regions of coordinately expressed genes. Nucleic Acids Res 33, W408-W411 (2005). TRED Catalogue of Zhao, F., Xuan, Z., Liu, transcription L., & Zhang, M. Q. TRED: a regulatory Transcriptional Regulatory elements Element Database and a platform for in silico gene regulation studies. Nucleic Acids Res 33, D103-D107 (2005). Oncomine Repository and Rhodes, D. R. et al. Mining analysis of for regulatory programs in cancer the cancer transcriptome. microarray Nat. Genet. 37, 579-583 data (2005). ABS Database of Bianco, E., Farre, D., regulatory Alba, M. M., Messeguer, X., & elements Guigo, R. ABS: a database of Annotated regulatory Binding Sites from orthologous promoters. Nucleic Acids Res 34, D63-D67 (2006). JASPAR Database of Sandelin, A., Alkema, W., regulatory Engstrom, P., Wasserman, W. elements W., & Lenhard, B. JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res 32, D91- D94 (2004). HTPSELEX Database of Jagannathan, V., Roulet, composite E., Delorenzi, M., & regulatory Bucher, P. HTPSELEX-a elements database of high-throughput SELEX libraries for transcription factor binding sites. Nucleic Acids Res 34, D90-D94 (2006). PReMod Database of Blanchette, M. et al. transcriptional Genome-wide computational regulatory prediction of transcriptional modules in the regulatory modules reveals human genome new insights into human gene expression. Genome Res 16, 656-668 (2006). CisView Browser of Sharov, A. A., Dudekula, regulatory D. B., & Ko, M. S. Cis- motifs and View: a browser and regions in the database of cis-regulatory genome modules predicted in the mouse genome. DNA Res 13, 123-134 (2006). BEARR Batch extraction Vega, V. B., Bangarusamy, algorithm D. K., Miller, L. D., Liu, for microarray E. T., & Lin, C. Y. BEARR: data analysis Batch Extraction and Analysis of cis-Regulatory Regions. Nucleic Acids Res 32, W257-W260 (2004). VISTA Align and compare Dubchak, I. & Rvaboy, D. sequences V. VISTA family of from multiple computational tools for species comparative analysis of DNA sequences and whole genomes. Methods Mol. Biol. 338, 69-89 (2006). PromAn Promoter analysis Lardenois, A. et al. PromAn: by integrating an integrated knowledge-based a variety of web server dedicated to databases promoter analysis. Nucleic Acids Res 34, W578-W583 (2006). CRSD Composite Liu, C. C. et al. CRSD: a regulatory comprehensive web server signature database for composite regulatory signature discovery. Nucleic Acids Res 34, W571-W577 (2006). MPromDb Portal for Sun, H. et al. MPromDb: an genome-wide integrated resource for promoter analysis annotation and visualization of mammalian gene promoters and ChIP-chip experimental data. Nucleic Acids Res 34, D98-103 (2006).

TABLE 2 Accesion Chromosome Expression Number Location Levels TOP2A* NM_001067 17q21-q22 E2F5 NM_001951 8q21.2 PRDX4 NM_006406 Xp22.11 + SMARCA4* NM_003072 19p13.2 PLK NM_005030 16p12.1 +++ KPNA2 NM_002266 17q24.2 CCT5 NM_012073 5p15.2 TRAF4* NM_004295 17q11-q12 E2-EPF M91670 19q13.43 +++ G3BP NM_005754 5q33.1 ++ PSME2 NM_002818 14q11.2 CDC2 NM_001786 10q21.1 ++ MCM3 NM_002388 6p12 + LDHA NM_005566 11p15.4 MMP9 NM_004994 20q11.2-q13.1 + HDAC1 NM_004964 1p34 COL1A2 NM_000089 7q22.1

TABLE 3 Frequency SYN value Sequence Identifiers Gene % Trans Factors  90/5852 0.593426961 ATGCAAAT TFE1: gg-5, gg-7, 4/7 POU2F1, POU2F2, gg-9, gg-10, gg-11, gamma-OBP, il3-11, igh-2, u2sn-6, GATA-1, NF-A, u2sn-1 Octa-factor 154/5852 0.594724494 TGACTCA TFE2: gm-cst-16, 7/7 Ap-1, c-fos, ag-10, cycd1-01, c-jun, NF-E2, txj-41, gfap-04, AP-2alphaA, MT-2A-08, ag-07 Fra-1, v-Jun  80/5852 0.58475401 ATTTGCAT TFE3: h2b-01, igk1-5, 4/7 PUR2F1, POU2F2, igk1-6, m2dra-4, CREN, NF-A, u4csn-1, m2dra-5, Oct-B2, m2dra-6, lp1-3 165/5852 0.600616265 AACAAAG TFE4: sry-01, ada-08, 7/7 SRY, LEF-1, cd3e-01 Matl-Mc, Sox-13, Sox-5, TCF-1, TCF-1A, TCF-1B, TCF-1C, TCF-1E, TCF-1F, TCF-1G  88/5852 0.549031051 TGAGTCA TFE5: bg-50, il3-02, 7/7 Ap-1, c-fos, ag-11, mmp3-02 c-jun, MafG, NF-E2  89/5852 0.629072127 CCTCCCAAA TFE6: cd8a-03 7/7 LyF-1  45/5852 0.544174923 TGACATCA TFE7: pth-01, cjun-12, 6/7 CREB, c-jun, act2-01 AP-1  90/5852 0.550796498 TTCAAAG TFE8: cd3E-02, 7/7 TCF-4, SRY, mmp7-02, tcf1-01 TCF-1A  69/5852 0.530281402 CTTTGAT TFE9: c-myc-19, 5/7 TCF-4E c-myc-20, cycD1   9/5852 0.723347374 GCTGGGATTA TFE10: plod1-02 5/7 PITX2 (SEQ ID CAGGTGTGAG No. 1) Anti- sense: SEQ ID No. 67

TABLE 4 Level of beta-galactosidase activity Mucin CRCSEs Cell Line Source CMV-beta Promoter (synthetic) HT29 Human Colon +++ ++ +++ Adenocarcinoma HCT116 Human Colon ++ + ++ Carcinoma DLD-1 Human Colon ++ + ++ Adenocarcinoma RKO Human Colon + +/− +/− Carcinoma NEURO2A Mouse +++ − − Neuroblastoma NIH3T3 Mouse +++ − − Fibroblasts CV1 Monkey ++ − − Fibroblasts HELA Human Ovarian ++ − − Cancer COS-7 Monkey ++ − − Fibroblasts MCF-7 Human Breast +++ − − Cancer Panc-1 Human Pancreatic ++ − − Cancer

TABLE 5 Promoter Sense strand (S) Anti-sense strand (AS) TFES (homology) Position No. 001 (CRCSE-1): TFE4 (86%) 224-230 (AS) S: SEQ-ID No. 5 TFE4 (86%) 51-57 (S)  AS: SEQ-ID No. 68 TFE10 (100%) 184-203 (AS) No. 102 TFE9 (100%) 68-74 (S)  S: SEQ-ID No. 6 TFE1C (95%) 236-256 (AS) AS: SEQ-ID No. 69 TFE8 (100%) 107-113 (S)  TFE5 (100%) 120-126 (S)  TFE1 (100%) 146-153 (S)  No. 103 TFE6 (100%) 208-216 (AS) S: SEQ-ID No. 7 TFE5 (100%) 195-201 (AS) AS: SEQ-ID No. 70 TFE8 (100%) 182-188 (AS) No. 105 TFE8 (100%) 250-256 (AS) S: SEQ-ID No. 8 TFE1 (100%) 222-230 (AS) AS: SEQ-ID No. 71 TFE9 (100%) 136-142 (S)  TFE1 (100%) 196-203 (AS) TFE9 (100%) 184-190 (AS) No. 106 TFE4 (86%) 288-294 (AS) S: SEQ-ID No. 9 TFE4 (86%) 198-204 (AS) AS: SEQ-ID No. 72 TFE4 (86%) 103-109 (S)  No. 108 TFE4 (86%) 198-404 (AS) S: SEQ-ID No. 10 TFE4 (86%) 48-54 (S)  AS: SEQ-ID No. 73

TABLE 6 Seq-ID No. without Seq-ID No. MUC-1 min. prom. Promoter Sense/Antisense Sense/Antisense No. TFE Pro_200 11/74 136/198 5 Pro_201 12/75 137/199 4 Pro_202 13/76 138/200 9 Pro_203 14/77 139/201 1 Pro_204 15/78 140/202 3 Pro_205 16/79 141/203 2 Pro_206 17/80 142/204 3 Pro_207 18/81 143/205 1 Pro_208 19/82 144/206 2 Pro_209 20/83 145/207 3 Pro_210 21/84 146/208 1 Pro_211 22/85 147/209 2 Pro_212 23/86 148/210 1 Pro_213 24/87 149/211 6 Pro_214 25/88 150/212 4 Pro_215 26/89 151/213 5 Pro_216 27/90 152/214 4 Pro_217 28/91 153/215 5 Pro_218 29/92 154/216 1 Pro_219 30/93 155/217 5 Pro_220 31/94 156/218 1 Pro_221 32/95 157/219 2 Pro_222 33/96 158/220 4 Pro_223 34/97 159/221 8 Pro_224 35/98 160/222 2 Pro_225 36/99 161/223 6 Pro_226  37/100 162/224 7 Pro_227  38/101 163/225 5 Pro_228  39/102 164/226 3 Pro_229  40/103 165/227 4 Pro_230  41/104 166/228 4 Pro_231  42/105 167/229 5 Pro_232  43/106 168/230 4 Pro_233  44/107 169/231 4 Pro_234  45/108 170/232 6 Pro_235  46/109 171/233 5 Pro_236  47/110 172/234 3 Pro_237  48/111 173/235 11  Pro_238  49/112 174/236 3 Pro_239  50/113 175/237 6 Pro_240  51/114 176/238 4 Pro_241  52/115 177/239 1 Pro_242  53/116 178/240 6 Pro_243  54/117 179/241 2 Pro_244  55/118 180/242 2 Pro_245  56/119 181/243 3 Pro_246  57/120 182/244 4 Pro_247  58/121 183/245 6 Pro_248  59/122 184/246 8 Pro_249  60/123 185/247 2 Pro_250  61/124 186/248 5 Pro_251  62/125 187/249 1 Pro_252  63/126 188/250 4 Pro_253  64/127 189/251 3 Pro_254  65/128 190/252 4 Pro_255  66/129 191/253    2 (86%)

TABLE 7 Table 7: Name cis Promoter Element Sequence Start End [bp] Direction Pro_200 TFE2 TGACTCA  72  78  7 reverse Pro_200 TFE2 (AS) TGAGTCA  72  78  7 forward Pro_200 TFE3 ATTTGCAT  58  65  8 forward Pro_200 TFE3 (AS) ATGCAAAT  58  65  8 reverse Pro_200 TFE4 AACAAAG  45  51  7 forward Pro_200 TFE4 AACAAAG  85  91  7 reverse Pro_200 TFE4 (AS) CTTTGTT  45  51  7 reverse Pro_200 TFE4 (AS) CTTTGTT  85  91  7 forward Pro_200 TFE7 TGACATCA  98 105  8 forward Pro_200 TFE7 (AS) TGATGTCA  98 105  8 reverse Pro_201 TFE1 ATGCAAAT  72  79  8 forward Pro_201 TFE1 ATGCAAAT  86  93  8 reverse Pro_201 TFE1 (AS) ATTTGCAT  72  79  8 reverse Pro_201 TFE1 (AS) ATTTGCAT  86  93  8 forward Pro_201 TFE4 AACAAAG  59  65  7 forward Pro_201 TFE4 (AS) CTTTGTT  59  65  7 reverse Pro_201 TFE7 TGACATCA  45  52  8 reverse Pro_201 TFE7 (AS) TGATGTCA  45  52  8 forward Pro_202 TFE1 ATGCAAAT  84  91  8 reverse Pro_202 TFE1 (AS) ATTTGCAT  84  91  8 forward Pro_202 TFE2 TGACTCA  58  64  7 reverse Pro_202 TFE2 TGACTCA  71  77  7 forward Pro_202 TFE2 TGACTCA 113 119  7 forward Pro_202 TFE2 (AS) TGAGTCA  58  64  7 forward Pro_202 TFE2 (AS) TGAGTCA  71  77  7 reverse Pro_202 TFE2 (AS) TGAGTCA 113 119  7 reverse Pro_202 TFE4 AACAAAG  45  51  7 forward Pro_202 TFE4 AACAAAG 126 132  7 forward Pro_202 TFE4 (AS) CTTTGTT  45  51  7 reverse Pro_202 TFE4 (AS) CTTTGTT 126 132  7 reverse Pro_202 TFE6 CCTCCCAAA  98 106  9 forward Pro_202 TFE6 (AS) TTTGGGAGG  98 106  9 reverse Pro_202 TFE8 TTCAAAG 152 158  7 reverse Pro_202 TFE8 (AS) CTTTGAA 152 158  7 forward Pro_202 TFE9 CTTTGAT 139 145  7 forward Pro_202 TFE9 (AS) ATCAAAG 139 145  7 reverse Pro_203 TFE4 AACAAAG  45  51  7 forward Pro_203 TFE4 (AS) CTTTGTT  45  51  7 reverse Pro_204 TFE1 ATGCAAAT  60  67  8 forward Pro_204 TFE1 (AS) ATTTGCAT  60  67  8 reverse Pro_204 TFE2 TGACTCA  74  80  7 reverse Pro_204 TFE2 (AS) TGAGTCA  74  80  7 forward Pro_204 TFE6 CCTCCCAAA  45  53  9 reverse Pro_204 TFE6 (AS) TTTGGGAGG  45  53  9 forward Pro_205 TFE2 TGACTCA  45  51  7 forward Pro_205 TFE2 (AS) TGAGTCA  45  51  7 reverse Pro_205 TFE10 GCTGGGATTA  58  77 20 reverse CAGGTGTGAG Pro_205 TFE10 (AS) CTCACACCTG  58  77 20 forward TAATCCCAGC Pro_206 TFE4 AACAAAG  58  64  7 forward Pro_206 TFE4 (AS) CTTTGTT  58  64  7 reverse Pro_206 TFE8 TTCAAAG  45  51  7 reverse Pro_206 TFE8 (AS) CTTTGAA  45  51  7 forward Pro_206 TFE10 GCTGGGATTA  71  90 20 reverse CAGGTGTGAG Pro_206 TFE10 (AS) CTCACACCTG  71  90 20 forward TAATCCCAGC Pro_207 TFE10 GCTGGGATTA  45  64 20 reverse CAGGTGTGAG Pro_207 TFE10 (AS) CTCACACCTG  45  64 20 forward TAATCCCAGC Pro_208 TFE6 CCTCCCAAA  45  53  9 reverse Pro_208 TFE6 (AS) TTTGGGAGG  45  53  9 forward Pro_208 TFE7 TGACATCA  60  67  8 reverse Pro_208 TFE7 (AS) TGATGTCA  60  67  8 forward Pro_209 TFE8 TTCAAAG  37  43  7 forward Pro_209 TFE8 (AS) CTTTGAA  37  43  7 reverse Pro_209 TFE9 CTTTGAT  50  56  7 forward Pro_209 TFE9 (AS) ATCAAAG  50  56  7 reverse Pro_209 TFE10 GCTGGGATTA  63  82 20 forward CAGGTGTGAG Pro_209 TFE10 (AS) CTCACACCTG  63  82 20 reverse TAATCCCAGC Pro_210 TFE1 ATGCAAAT  58  65  8 reverse Pro_210 TFE1 (AS) ATTTGCAT  58  65  8 forward Pro_211 TFE2 TGACTCA  45  51  7 forward Pro_211 TFE2 (AS) TGAGTCA  45  51  7 reverse Pro_211 TFE4 AACAAAG  58  64  7 reverse Pro_211 TFE4 (AS) CTTTGTT  58  64  7 forward Pro_212 TFE6 CCTCCCAAA  45  53  9 forward Pro_212 TFE6 (AS) TTTGGGAGG  45  53  9 reverse Pro_213 TFE2 TGACTCA 125 131  7 forward Pro_213 TFE2 (AS) TGAGTCA 125 131  7 reverse Pro_213 TFE4 AACAAAG  45  51  7 forward Pro_213 TFE4 (AS) AACAAAG  58  64  7 reverse Pro_213 TFE4 CTTTGTT  45  51  7 reverse Pro_213 TFE4 (AS) CTTTGTT  58  64  7 forward Pro_213 TFE6 CCTCCCAAA  71  79  9 reverse Pro_213 TFE6 (AS) TTTGGGAGG  71  79  9 forward Pro_213 TFE9 CTTTGAT 112 118  7 reverse Pro_213 TFE9 (AS) ATCAAAG 112 118  7 forward Pro_213 TFE10 GCTGGGATTA  86 105 20 forward CAGGTGTGAG Pro_213 TFE10 (AS) CTCACACCTG  86 105 20 reverse TAATCCCAGC Pro_214 TFE1 ATGCAAAT  72  79  8 forward Pro_214 TFE1 (AS) ATTTGCAT  72  79  8 reverse Pro_214 TFE2 TGACTCA  86  92  7 reverse Pro_214 TFE2 (AS) TGAGTCA  86  92  7 forward Pro_214 TFE7 TGACATCA  45  52  8 forward Pro_214 TFE7 (AS) TGATGTCA  45  52  8 reverse Pro_214 TFE8 TTCAAAG  59  65  7 forward Pro_214 TFE8 (AS) CTTTGAA  59  65  7 reverse Pro_215 TFE1 ATGCAAAT  71  78  8 reverse Pro_215 TFE1 ATGCAAAT  99 106  8 reverse Pro_215 TFE1 (AS) ATTTGCAT  71  78  8 forward Pro_215 TFE1 (AS) ATTTGCAT  99 106  8 forward Pro_215 TFE4 AACAAAG  58  64  7 forward Pro_215 TFE4 (AS) CTTTGTT  58  64  7 reverse Pro_215 TFE6 CCTCCCAAA 113 121  9 forward Pro_215 TFE6 (AS) TTTGGGAGG 113 121  9 reverse Pro_215 TFE7 TGACATCA  85  92  8 forward Pro_215 TFE7 (AS) TGATGTCA  85  92  8 reverse Pro_216 TFE1 ATGCAAAT  45  52  8 forward Pro_216 TFE1 (AS) ATTTGCAT  45  52  8 reverse Pro_216 TFE5 TGAGTCA  59  65  7 forward Pro_216 TFE5 TGAGTCA  85  91  7 forward Pro_216 TFE5 (AS) TGACTCA  59  65  7 reverse Pro_216 TFE5 (AS) TGACTCA  85  91  7 reverse Pro_216 TFE8 TTCAAAG  72  78  7 forward Pro_216 TFE8 (AS) CTTTGAA  72  78  7 reverse Pro_217 TFE2 TGACTCA  84  90  7 reverse Pro_217 TFE2 (AS) TGAGTCA  84  90  7 forward Pro_217 TFE4 AACAAAG  71  77  7 forward Pro_217 TFE4 (AS) CTTTGTT  71  77  7 reverse Pro_217 TFE6 CCTCCCAAA 110 118  9 reverse Pro_217 TFE6 (AS) TTTGGGAGG 110 118  9 forward Pro_217 TFE8 TTCAAAG  97 103  7 reverse Pro_217 TFE8 (AS) CTTTGAA  97 103  7 forward Pro_217 TFE10 GCTGGGATTA  45  64 20 forward CAGGTGTGAG Pro_217 TFE10 (AS) CTCACACCTG  45  64 20 reverse TAATCCCAGC Pro_218 TFE4 AACAAAG  58  64  7 reverse Pro_218 TFE4 (AS) CTTTGTT  58  64  7 forward Pro_219 TFE6 CCTCCCAAA  72  80  9 reverse Pro_219 TFE6 (AS) TTTGGGAGG  72  80  9 forward Pro_219 TFE7 TGACATCA  45  52  8 forward Pro_219 TFE7 (AS) TGATGTCA  45  52  8 reverse Pro_219 TFE8 TTCAAAG 100 106  7 forward Pro_219 TFE8 (AS) CTTTGAA 100 106  7 reverse Pro_219 TFE9 CTTTGAT  59  65  7 reverse Pro_219 TFE9 CTTTGAT  89  95  7 forward Pro_219 TFE9 (AS) ATCAAAG  59  65  7 forward Pro_219 TFE9 (AS) ATCAAAG  89  95  7 reverse Pro_220 TFE2 TGACTCA  45  51  7 forward Pro_220 TFE2 (AS) TGAGTCA  45  51  7 reverse Pro_221 TFE1 ATGCAAAT  58  65  8 forward Pro_221 TFE1 (AS) ATTTGCAT  58  65  8 reverse Pro_221 TFE2 TGACTCA  45  51  7 reverse Pro_221 TFE2 (AS) TGAGTCA  45  51  7 forward Pro_222 TFE1 ATGCAAAT 123 130  8 forward Pro_222 TFE1 (AS) ATTTGCAT 123 130  8 reverse Pro_222 TFE2 TGACTCA  97 103  7 reverse Pro_222 TFE2 (AS) TGAGTCA  97 103  7 forward Pro_222 TFE8 TTCAAAG  84  90  7 forward Pro_222 TFE8 (AS) CTTTGAA  84  90  7 reverse Pro_222 TFE9 CTTTGAT  45  51  7 forward Pro_222 TFE9 (AS) ATCAAAG  45  51  7 reverse Pro_223 TFE2 (AS) TGAGTCA 126 132  7 forward Pro_223 TFE3 ATTTGCAT  98 105  8 forward Pro_223 TFE3 ATTTGCAT 112 119  8 reverse Pro_223 TFE3 (AS) ATGCAAAT  98 105  8 reverse Pro_223 TFE3 (AS) ATGCAAAT 112 119  8 forward Pro_223 TFE5 TGAGTCA  72  78  7 reverse Pro_223 TFE5 TGAGTCA  85  91  7 forward Pro_223 TFE5 TGAGTCA 126 132  7 forward Pro_223 TFE5 (AS)  TGACTCA  72  78  7 forward Pro_223 TFE5 (AS) TGACTCA  85  91  7 reverse Pro_223 TFE7 TGACATCA  45  52  8 reverse Pro_223 TFE7 (AS) TGAGGTCA  45  52  8 forward Pro_223 TFE9 CTTTGAT  59  65  7 forward Pro_223 TFE9 (AS) ATCAAAG  59  65  7 reverse Pro_224 TFE2 TGACTCA  72  78  7 reverse Pro_224 TFE2 (AS) TGAGTCA  72  78  7 forward Pro_224 TFE7 TGACATCA  45  52  8 reverse Pro_224 TFE7 (AS) TGATGTCA  45  52  8 forward Pro_225 TFE1 ATGCAAAT  71  78  8 forward Pro_225 TFE1 (AS) ATTTGCAT  71  78  8 reverse Pro_225 TFE4 AACAAAG  58  64  7 reverse Pro_225 TFE4 (AS) CTTTGTT  58  64  7 forward Pro_225 TFE5 TGAGTCA  85  91  7 reverse Pro_225 TFE5 (AS) TGACTCA  85  91  7 forward Pro_225 TFE6 CCTCCCAAA  98 106  9 reverse Pro_225 TFE6 (AS) TTTGGGAGG  98 106  9 forward Pro_225 TFE8 TTCAAAG  45  51  7 reverse Pro_225 TFE8 (AS) CTTTGAA  45  51  7 forward Pro_225 TFE9 CTTTGAT  38  44  7 forward Pro_225 TFE9 (AS) ATCAAAG  38  44  7 reverse Pro_226 TFE2 TGACTCA 123 129  7 reverse Pro_226 TFE2 (AS) TGAGTCA 123 129  7 forward Pro_226 TFE4 AACAAAG  58  64  7 reverse Pro_226 TFE4 AACAAAG 110 116  7 forward Pro_226 TFE4 (AS) CTTTGTT  58  64  7 forward Pro_226 TFE4 (AS) CTTTGTT 110 116  7 reverse Pro_226 TFE8 TTCAAAG  45  51  7 forward Pro_226 TFE8 TTCAAAG  71  77  7 forward Pro_226 TFE8 TTCAAAG  97 103  7 forward Pro_226 TFE8 (AS) CTTTGAA  45  51  7 reverse Pro_226 TFE8 (AS) CTTTGAA  71  77  7 reverse Pro_226 TFE8 (AS) CTTTGAA  97 103  7 reverse Pro_226 TFE9 CTTTGAT 136 142  7 reverse Pro_226 TFE9 (AS) ATCAAAG 136 142  7 forward Pro_227 TFE1 ATGCAAAT  45  52  8 reverse Pro_227 TFE1 (AS) ATTTGCAT  45  52  8 forward Pro_227 TFE3 ATTTGCAT  99 106  8 forward Pro_227 TFE3 (AS) ATGCAAAT  99 106  8 reverse Pro_227 TFE4 AACAAAG  72  78  7 reverse Pro_227 TFE4 (AS) CTTTGTT  72  78  7 forward Pro_227 TFE5 TGAGTCA  59  65  7 forward Pro_227 TFE5 (AS) TGACTCA  59  65  7 reverse Pro_227 TFE7 TGACATCA  85  92  8 reverse Pro_227 TFE7 (AS) TGATGTCA  85  92  8 forward Pro_228 TFE6 CCTCCCAAA  44  52  9 forward Pro_228 TFE6 (AS) TTTGGGAGG  44  52  9 reverse Pro_228 TFE7 TGACATCA  85  92  8 forward Pro_228 TFE7 (AS) TGATGTCA  85  92  8 reverse Pro_228 TFE8 TTCAAAG  59  65  7 forward Pro_228 TFE8 (AS) CTTTGAA  59  65  7 reverse Pro_229 TFE1 ATGCAAAT  71  78  8 reverse Pro_229 TFE1 (AS) ATTTGCAT  71  78  8 forward Pro_229 TFE5 TGAGTCA  45  51  7 forward Pro_229 TFE5 TGAGTCA  58  64  7 forward Pro_229 TFE5 (AS) TGACTCA  45  51  7 reverse Pro_229 TFE5 (AS) TGACTCA  58  64  7 reverse Pro_229 TFE8 TTCAAAG  85  91  7 forward Pro_229 TFE8 (AS) CTTTGAA  85  91  7 reverse Pro_230 TFE3 ATTTGCAT  45  52  8 forward Pro_230 TFE3 ATTTGCAT  59  66  8 forward Pro_230 TFE3 ATTTGCAT  86  93  8 reverse Pro_230 TFE3 (AS) ATGCAAAT  45  52  8 reverse Pro_230 TFE3 (AS) ATGCAAAT  59  66  8 reverse Pro_230 TFE3 (AS) ATGCAAAT  86  93  8 forward Pro_230 TFE5 TGAGTCA  73  79  7 forward Pro_230 TFE5 (AS) TGACTCA  73  79  7 reverse Pro_231 TFE1 ATGCAAAT  72  79  8 forward Pro_231 TFE1 (AS) ATTTGCAT  72  79  8 reverse Pro_231 TFE4 AACAAAG  59  65  7 reverse Pro_231 TFE4 (AS) CTTTGTT  59  65  7 forward Pro_231 TFE5 TGAGTCA  86  92  7 reverse Pro_231 TFE5 (AS) TGACTCA  86  92  7 forward Pro_231 TFE6 CCTCCCAAA  99 107  9 reverse Pro_231 TFE6 (AS) TTTGGGAGG  99 107  9 forward Pro_231 TFE7 TGACATCA  45  52  8 reverse Pro_231 TFE7 (AS) TGATGTCA  45  52  8 forward Pro_232 TFE1 ATGCAAAT  53  60  8 reverse Pro_232 TFE1 (AS) ATTTGCAT  53  60  8 forward Pro_232 TFE3 ATTTGCAT  67  74  8 forward Pro_232 TFE3 (AS) ATGCAAAT  67  74  8 reverse Pro_232 TFE5 TGAGTCA 107 113  7 reverse Pro_232 TFE5 (AS) TGACTCA 107 113  7 forward Pro_232 TFE8 TTCAAAG  94 100  7 reverse Pro_232 TFE8 (AS) CTTTGAA  94 100  7 forward Pro_233 TFE1 ATGCAAAT  58  65  8 forward Pro_233 TFE1 (AS) ATTTGCAT  58  65  8 reverse Pro_233 TFE4 AACAAAG  45  51  7 forward Pro_233 TFE4 AACAAAG  72  78  7 forward Pro_233 TFE4 (AS) CTTTGTT  45  51  7 reverse Pro_233 TFE4 (AS) CTTTGTT  72  78  7 reverse Pro_233 TFE8 TTCAAAG  85  91  7 reverse Pro_233 TFE8 (AS) CTTTGAA  85  91  7 forward Pro_234 TFE5 TGAGTCA  49  55  7 forward Pro_234 TFE5 TGAGTCA  62  68  7 reverse Pro_234 TFE5 TGAGTCA 103 109  7 forward Pro_234 TFE5 (AS) TGACTCA  49  55  7 reverse Pro_234 TFE5 (AS) TGACTCA  62  68  7 forward Pro_234 TFE5 (AS) TGACTCA 103 109  7 reverse Pro_234 TFE6 CCTCCCAAA  88  96  9 forward Pro_234 TFE6 (AS) TTTGGGAGG  88  96  9 reverse Pro_234 TFE7 TGACATCA 116 123  8 forward Pro_234 TFE7 TGACATCA 130 137  8 reverse Pro_234 TFE7 (AS) TGATGTCA 116 123  8 reverse Pro_234 TFE7 (AS) TGATGTCA 130 137  8 forward Pro_235 TFE3 ATTTGCAT  45  52  8 forward Pro_235 TFE3 (AS) ATGCAAAT  45  52  8 reverse Pro_235 TFE4 AACAAAG  72  78  7 reverse Pro_235 TFE4 (AS) CTTTGTT  72  78  7 forward Pro_235 TFE5 TGAGTCA  59  65  7 forward Pro_235 TFE5 TGAGTCA  85  91  7 forward Pro_235 TFE5 TGAGTCA  98 104  7 forward Pro_235 TFE5 (AS) TGACTCA  59  65  7 reverse Pro_235 TFE5 (AS) TGACTCA  85  91  7 reverse Pro_235 TFE5 (AS) TGACTCA  98 104  7 reverse Pro_236 TFE1 ATGCAAAT  72  79  8 forward Pro_236 TFE1 (AS) ATTTGCAT  72  79  8 reverse Pro_236 TFE2 TGACTCA  59  65  7 reverse Pro_236 TFE2 (AS) TGAGTCA  59  65  7 forward Pro_236 TFE7 TGACATCA  45  52  8 reverse Pro_236 TFE7 (AS) TGATGTCA  45  52  8 forward Pro_237 TFE2 TGACTCA  99 105  7 forward Pro_237 TFE2 (AS) TGAGTCA  99 105  7 reverse Pro_237 TFE3 ATTTGCAT  57  64  8 forward Pro_237 TFE3 ATTTGCAT  71  78  8 forward Pro_237 TFE3 (AS) ATGCAAAT  57  64  8 reverse Pro_237 TFE3 (AS) ATGCAAAT  71  78  8 reverse Pro_237 TFE4 AACAAAG  18  24  7 forward Pro_237 TFE4 (AS) CTTTGTT  18  24  7 reverse Pro_237 TFE5 TGAGTCA  31  37  7 reverse Pro_237 TFE5 TGAGTCA  44  50  7 forward Pro_237 TFE5 (AS) TGACTCA  31  37  7 forward Pro_237 TFE5 (AS) TGACTCA  44  50  7 reverse Pro_237 TFE6 CCTCCCAAA 165 173  9 forward Pro_237 TFE6 (AS) TTTGGGAGG 165 173  9 reverse Pro_237 TFE7 TGACATCA  85  92  8 forward Pro_237 TFE7 TGACATCA 112 119  8 forward Pro_237 TFE7 (AS) TGATGTCA  85  92  8 reverse Pro_237 TFE7 (AS) TGATGTCA 112 119  8 reverse Pro_237 TFE8 TTCAAAG 126 132  7 forward Pro_237 TFE8 (AS) CTTTGAA 126 132  7 reverse Pro_237 TFE10 GCTGGGATTA 139 158 20 reverse CAGGTGTGAG Pro_237 TFE10 (AS) CTCACACCTG 139 158 20 forward TAATCCCAGC Pro_238 TFE1 ATGCAAAT  58  65  8 forward Pro_238 TFE1 (AS) ATTTGCAT  58  65  8 reverse Pro_238 TFE8 TTCAAAG  45  51  7 reverse Pro_238 TFE8 (AS) CTTTGAA  45  51  7 forward Pro_238 TFE9 CTTTGAT  38  44  7 forward Pro_238 TFE9 (AS) ATCAAAG  38  44  7 reverse Pro_239 TFE1 ATGCAAAT  96 103  8 reverse Pro_239 TFE1 ATGCAAAT 124 131  8 forward Pro_239 TFE1 (AS) ATTTGCAT  96 103  8 forward Pro_239 TFE1 (AS) ATTTGCAT 124 131  8 reverse Pro_239 TFE4 AACAAAG  18  24  7 reverse Pro_239 TFE4 AACAAAG  44  50  7 forward Pro_239 TFE4 (AS) CTTTGTT  18  24  7 forward Pro_239 TFE4 (AS) CTTTGTT  44  50  7 reverse Pro_239 TFE5 TGAGTCA  31  37  7 forward Pro_239 TFE5 (AS) TGACTCA  31  37  7 reverse Pro_239 TFE9 CTTTGAT  57  63  7 reverse Pro_239 TFE9 (AS) ATCAAAG  57  63  7 forward Pro_240 TFE1 ATGCAAAT  78  85  8 reverse Pro_240 TFE1 (AS) ATTTGCAT  78  85  8 forward Pro_240 TFE7 TGACATCA  92  99  8 forward Pro_240 TFE7 (AS) TGATGTCA  92  99  8 reverse Pro_240 TFE10 GCTGGGATTA  14  33 20 forward CAGGTGTGAG Pro_240 TFE10 GCTGGGATTA  52  71 20 forward CAGGTGTGAG Pro_240 TFE10 (AS) CTCACACCTG  14  33 20 reverse TAATCCCAGC Pro_240 TFE10 (AS) CTCACACCTG  33  71 20 reverse TAATCCCAGC Pro_241 TFE9 CTTTGAT  58  64  7 reverse Pro_241 TFE9 (AS) ATCAAAG  58  64  7 forward Pro_242 TFE2 TGACTCA 121 127  7 reverse Pro_242 TFE2 (AS) TGAGTCA 121 127  7 forward Pro_242 TFE3 ATTTGCAT  71  78  8 reverse Pro_242 TFE3 (AS) ATGCAAAT  71  78  8 forward Pro_242 TFE5 TGAGTCA  58  64  7 reverse Pro_242 TFE5 TGAGTCA  85  91  7 forward Pro_242 TFE5 TGAGTCA  98 104  7 reverse Pro_242 TFE5 (AS) TGACTCA  58  64  7 forward Pro_242 TFE5 (AS) TGACTCA  85  91  7 reverse Pro_242 TFE5 (AS) TGACTCA  98 104  7 forward Pro_242 TFE7 TGACATCA 111 118  8 reverse Pro_242 TFE7 (AS) TGATGTCA 111 118  8 forward Pro_243 TFE1 ATGCAAAT  45  52  8 reverse Pro_243 TFE1 ATGCAAAT  59  66  8 forward Pro_243 TFE1 (AS) ATTTGCAT  45  52  8 forward Pro_243 TFE1 (AS) ATTTGCAT  59  66  8 reverse Pro_244 TFE1 ATGCAAAT  60  67  8 revere Pro_244 TFE1 (AS) ATTTGCAT  60  67  8 forward Pro_244 TFE6 CCTCCCAAA  45  53  9 forward Pro_244 TFE6 (AS) TTTGGGAGG  45  53  9 forward Pro_245 TFE7 TGACATCA  45  52  8 reverse Pro_245 TFE7 (AS) TGATGTCA  45  52  8 forward Pro_245 TFE8 TTCAAAG  59  65  7 forward Pro_245 TFE8 TTCAAAG  72  78  7 reverse Pro_245 TFE8 (AS) CTTTGAA  59  65  7 reverse Pro_245 TFE8 (AS) CTTTGAA  72  78  7 forward Pro_246 TFE1 ATGCAAAT  58  65  8 reverse Pro_246 TFE1 (AS) ATTTGCAT  58  65  8 forward Pro_246 TFE3 ATTTGCAT  87  94  8 reverse Pro_246 TFE3 (AS) ATGCAAAT  87  94  8 forward Pro_246 TFE5 TGAGTCA 101 107  7 reverse Pro_246 TFE5 (AS) TGACTCA 101 107  7 forward Pro_246 TFE6 CCTCCCAAA  72  80  9 reverse Pro_246 TFE6 (AS) TTTGGGAGG  72  80  9 forward Pro_247 TFE1 ATGCAAAT 100 107  8 reverse Pro_247 TFE1 (AS) ATTTGCAT 100 107  8 forward Pro_247 TFE4 AACAAAG  45  51  7 reverse Pro_247 TFE4 AACAAAG 127 133  7 forward Pro_247 TFE4 (AS) CTTTGTT  45  51  7 forward Pro_247 TFE4 (AS) CTTTGTT 127 133  7 reverse Pro_247 TFE6 CCTCCCAAA  71  79  9 reverse Pro_247 TFE6 (AS) TTTGGGAGG  71  79  9 forward Pro_247 TFE8 TTCAAAG  58  64  7 reverse Pro_247 TFE8 (AS) CTTTGAA  58  64  7 forward Pro_247 TFE9 CTTTGAT 114 120  7 reverse Pro_247 TFE9 (AS) ATCAAAG 114 120  7 forward Pro_248 TFE1 ATGCAAAT  45  52  8 reverse Pro_248 TFE1 (AS) ATTTGCAT  45  52  8 forward Pro_248 TFE4 AACAAAG  98 104  7 forward Pro_248 TFE4 AACAAAG 137 143  7 forward Pro_248 TFE4 (AS) CTTTGTT  98 104  7 reverse Pro_248 TFE4 (AS) CTTTGTT 137 143  7 reverse Pro_248 TFE5 TGAGTCA 111 117  7 forward Pro_248 TFE5 TGAGTCA 124 130  7 reverse Pro_248 TFE5 (AS) TGACTCA 111 117  7 reverse Pro_248 TFE5 (AS) TGACTCA 124 130  7 forward Pro_248 TFE8 TTCAAAG  85  91  7 reverse Pro_248 TFE8 (AS) CTTTGAA  85  91  7 forward Pro_248 TFE9 CTTTGAT 150 156  7 reverse Pro_248 TFE9 (AS) ATCAAAG 150 156  7 forward Pro_248 TFE10 GCTGGGATTA  59  78 20 forward CAGGTGTGAG Pro_248 TFE10 (AS) CTCACACCTG  59  78 20 reverse TAATCCCAGC Pro_249 TFE2 TGACTCA  58  64  7 reverse Pro_249 TFE2 (AS) TGAGTCA  58  64  7 forward Pro_249 TFE9 CTTTGAT  71  77  7 forward Pro_249 TFE9 (AS) ATCAAAG  71  77  7 reverse Pro_250 TFE2 TGACTCA  58  64  7 forward Pro_250 TFE2 (AS) TGAGTCA  58  64  7 reverse Pro_250 TFE4 AACAAAG  84  90  7 reverse Pro_250 TFE4 (AS) CTTTGTT  84  90  7 forward Pro_250 TFE8 TTCAAAG  45  51  7 forward Pro_250 TFE8 (AS) CTTTGAA  45  51  7 reverse Pro_250 TFE9 CTTTGAT  71  77  7 forward Pro_250 TFE9 (AS) ATCAAAG  71  77  7 reverse Pro_250 TFE10 GCTGGGATTA  97 116 20 reverse CAGGTGTGAG Pro_250 TFE10 (AS) CTCACACCTG 97 116 20 forward TAATCCCAGC Pro_251 TFE10 GCTGGGATTA  44  63 20 reverse CAGGTGTGAG Pro_251 TFE10 (AS) CTCACACCTG  44  63 20 forward TAATCCCAGC Pro_252 TFE1 ATGCAAAT 122 129  8 forward Pro_252 TFE1 (AS) ATTTGCAT 122 129  8 reverse Pro_252 TFE2 TGACTCA  96 102  7 reverse Pro_252 TFE2 (AS) TGAGTCA  96 102  7 forward Pro_252 TFE8 TTCAAAG  83  89  7 forward Pro_252 TFE8 (AS) CTTTGAA  83  89  7 reverse Pro_252 TFE9 CTTTGAT  44  50  7 forward Pro_252 TFE9 (AS) ATCAAAG  44  50  7 reverse Pro_253 TFE2 TGACTCA  60  66  7 reverse Pro_253 TFE2 (AS) TGAGTCA  60  66  7 forward Pro_253 TFE6 CCTCCCAAA  45  53  9 reverse Pro_253 TFE6 (AS) TTTGGGAGG  45  53  9 forward Pro_253 TFE8 TTCAAAG  73  79  7 reverse Pro_253 TFE8 (AS) CTTTGAA  73  79  7 forward Pro_254 TFE1 ATGCAAAT  84  91  8 reverse Pro_254 TFE1 ATGCAAAT 111 118  8 reverse Pro_254 TFE1 (AS) ATTTGCAT  84  91  8 forward Pro_254 TFE1 (AS) ATTTGCAT 111 118  8 forward Pro_254 TFE8 TTCAAAG  58  64  7 reverse Pro_254 TFE8 (AS) CTTTGAA  58  64  7 forward Pro_254 TFE9 CTTTGAT  98 104  7 forward Pro_254 TFE9 ATCAAAG 124 130  7 reverse Pro_254 TFE9 (AS) ATCAAAG  98 104  7 reverse Pro_254 TFE9 (AS) ATCAAAG 124 130  7 forward Pro_255 TFE4 AACAAAG  26  32  7 forward Pro_255 TFE4 (AS) CTTTGTT  26  32  7 reverse Pro_255 TFE4 AACAAAG  18  24  7 reverse Pro_255 TFE4 (AS) CTTTGTT  18  24  7 forward 

1-25. (canceled)
 26. A vector, comprising a combined promoter cassette, the combined promoter cassette comprising randomly combined elements, a minimum promoter, and a reporter gene, wherein each of the elements contains more than 80% sequence identity with a transcription factor regulatory element (TFRE) that is within 20 kilobases of more than fifty percent of a plurality of genes identified as being differentially expressed in a particular cell type or tissue type or under a particular condition and has a SYN value larger than 0.3, wherein the SYN value is defined as frequency^((1/length)), wherein frequency is the frequency of occurrence of the TFRE within 20 kilobases of any of the plurality of the genes and length is the length in nucleotides of the TFRE.
 27. The vector of claim 26, wherein the vector is a plasmid or viral vector.
 28. The vector of claim 26, wherein the vector is transiently expressed or integrated into a genome of a host cell.
 29. The vector of claim 26, wherein the frequency of occurrence of the TFRE within 20 kilobases of any of the plurality of the genes is the frequency of occurrence of the TFRE within 20 kilobases in the sense strand of any of the plurality of the genes.
 30. The vector of claim 26, wherein the vector does not comprise a promoter or regulatory sequences other than those present in the promoter cassette.
 31. The vector of claim 26, wherein the TFRE is associated with a plurality of genes, each of which is differentially expressed in a particular cell type or tissue type, or under a particular condition.
 32. The vector of claim 31, each of the plurality of genes is differentially expressed under a disease condition.
 33. The vector of claim 32, wherein the disease condition is cancer.
 34. The vector of claim 31, wherein each of the plurality of genes is differentially expressed under the particular condition, which is exposure to a particular biological agent, chemical agent, or microbial pathogen.
 35. The vector of claim 26, wherein the reported gene is LacZ or GFP.
 36. A vector library comprising a plurality of the vectors of claim
 26. 37. The vector library of claim 36, wherein the vector library is a retroviral vector library.
 38. A cell expressing a vector of claim
 26. 39. A cell expressing a vector library of claim
 36. 